# IM-3D: Iterative Multiview Diffusion and Reconstruction for
High-Quality 3D Generation

Luke Melas-Kyriazi  Iro Laina  Christian Rupprecht  Natalia Neverova  Andrea
Vedaldi  Oran Gafni  Filippos Kokkinos

Machine Learning, ICML

Figure 1: IM-3D generates high-quality and faithful 3D assets from text/image
pair. It eschews Score Distillation Sampling (SDS) for robust 3D
reconstruction of the output of a video diffusion model, tuned to generate a
360° video of the object.

Abstract

Most text-to-3D generators build upon off-the-shelf text-to-image models
trained on billions of images. They use variants of Score Distillation
Sampling (SDS), which is slow, somewhat unstable, and prone to artifacts. A
mitigation is to fine-tune the 2D generator to be multi-view aware, which can
help distillation or can be combined with reconstruction networks to output 3D
objects directly. In this paper, we further explore the design space of text-
to-3D models. We significantly improve multi-view generation by considering
video instead of image generators. Combined with a 3D reconstruction algorithm
which, by using Gaussian splatting, can optimize a robust image-based loss, we
directly produce high-quality 3D outputs from the generated views. Our new
method, IM-3D, reduces the number of evaluations of the 2D generator network
10-100$\times$, resulting in a much more efficient pipeline, better quality,
fewer geometric inconsistencies, and a high yield of usable 3D assets.

##  1 Introduction

All state-of-the-art open-world text-to-3D generators are built on top of off-
the-shelf 2D generators trained on billions of images. This is necessary
because there isn’t enough 3D training data to directly learn generators that
can understand language and operate in an open-ended manner. However, the best
way of building such models is still debated.

One approach is to perform 3D distillation by adopting Score Distillation
Sampling (SDS) (Poole et al., 2023) or one of its variants. These models can
work on top of nearly any modern 2D generator, but they require tens of
thousands of evaluations of the 2D generator, and can take hours to generate a
single asset. They are also prone to artifacts and may fail to converge.
Mitigating these shortcomings inspired a significant body of research (Wang et
al., 2023b).

The fundamental reason for these limitations is that the underlying 2D
generator is not 3D aware. SDS slowly makes the different views of the 3D
object agree with the 2D model, which characterizes them independently of each
other. Several authors (Shi et al., 2023b; Wang & Shi, 2023; Shi et al.,
2023a) have shown that fine-tuning the 2D generator to understand the
correlation between different views of the object significantly facilitates
distillation. More recently, approaches such as (Li et al., 2023) avoid
distillation entirely and instead just reconstruct the 3D object from the
generated views. However, in order to compensate for defects in multi-view
generation, they must incorporate very large 3D reconstruction networks.
Ultimately, these approaches are many times faster than distillation, but
quality is limited.

In this paper, we explore the benefits of further increasing the quality of
multi-view generation and how this might affect the design space of future
text-to-3D models. We are inspired by the fact that, in the limit, a 2D
generator could output enough consistent views of the object to afford simple
multi-view reconstruction, sidestepping distillation and reconstruction
networks entirely.

To this end, we introduce IM-3D, a text-to-3D generation approach that
leverages Iterative Multiview diffusion and reconstruction (Figures 1 and 2).
IM-3D is based on significantly boosting the quality of the multi-view
generation network by switching from a text-to-image to a text-to-video
generator network. Specifically, we pick Emu Video (Girdhar et al., 2023), a
video generator conditioned both on a reference image and a textual prompt.
Our first contribution is to show that Emu Video can be fine-tuned, using a
relatively small number of synthetic 3D assets, to generate directly up to 16
high-resolution consistent views ($512\times 512$) of the object. While Emu
Video is in itself an iterative model based on diffusion, by adopting a fast
sampling algorithm, the views can be generated in a few seconds and in a small
number of iterations.

Our second contribution is to show that we can extract a high-quality 3D
object by _directly_ fitting a 3D model to the resulting views—without
distillation or reconstruction networks—quickly and robustly. To do so, we
rely on a 3D reconstruction algorithm based on Gaussian splatting (GS) (Kerbl
et al., 2023). The importance of GS is that it affords fast differentiable
rendering of the 3D object, which allows the use of image-based losses like
LPIPS. The latter is key to bridging the small inconsistencies left by the 2D
generator without requiring ad-hoc reconstruction models.

Third, we notice that, while this process results in mostly very good 3D
models, some inconsistencies may still remain. We thus propose to close the
loop and feed the 3D reconstruction back to the 2D generator. In order to do
so, we simply render noised images of the 3D object and restart the video
diffusion process from those. This approach is closer in spirit to SDS as it
builds consensus progressively, but the feedback loop is closed two or three
times per generated asset, instead of tens of thousands of times.

There are many advantages to our approach. Compared to SDS, it reduces
dramatically the number of evaluations of the 2D generator network. Using a
fast sampler, generating the first version of the multi-view images requires
only around 40 evaluations. Iterated generations are much shorter (as they
start from a partially denoised result), at most doubling the total number of
evaluations. This is a 10-100$\times$ reduction compared to SDS. The 3D
reconstruction is also very fast, taking only a minute for the first version
of the asset, and a few seconds for the second or third. It also sidesteps
typical issues of the SDS such as artifacts (e.g., saturated colors, Janus
problem), lack of diversity (by avoiding mode seeking), and low yield (failure
to converge). Compared to methods like (Li et al., 2023), IM-3D is slower, but
achieves much higher quality, and does not require to learn large
reconstruction networks, offloading most of the work to 2D generation instead.

In a nutshell, our contribution is to show how video generator networks can
improve consistent multi-view generator to a point where it is possible to
obtain state-of-the-art and efficient text/image-to-3D results without
distillation and without training reconstruction networks.

##  2 Related work

#### 3D Distillation.

3D distillation is the process of extracting a 3D object from a 2D neural
network trained to generate images from text, or otherwise match them to text.
For example, methods like DreamFields (Jain et al., 2022) do so starting from
the CLIP image similarity score. However, most recent methods build on
diffusion-based image generators that utilize variants of the Score
Distillation Sampling (SDS) loss introduced with DreamFusion (Poole et al.,
2023). Fantasia 3D (Chen et al., 2023a) disentangles illumination from
materials. Magic3D (Lin et al., 2022) reconstructs high-resolution texture
meshes. RealFusion (Melas-Kyriazi et al., 2023) starts from a reference image
and fine-tunes the prompt of a 2D generator to match it, distilling a 3D
object afterwards. Make-it-3D (Tang et al., 2023b) also starts from a 2D
image, combining SDS with a CLIP loss with respect to the reference image and
a depth prior. HiFi-123 (Yu et al., 2023) uses DDIM inversion to obtain the
code for the reference image. ATT3D (Lorraine et al., 2023) develops an
amortized version of SDS, where several variants of the same object are
distilled in parallel. HiFA (Zhu & Zhuang, 2023) reformulates the SDS loss and
anneals the diffusion noise. DreamTime (Huang et al., 2023) also proposes to
optimize the noise schedule. ProlificDreamer (Wang et al., 2023b),
SteinDreamer (Wang et al., 2023a), Collaborative SDS (Kim et al., 2023) and
Noise-free SDS (Katzir et al., 2023) improve the variance of the SDS gradient
estimate. DreamGaussian (Tang et al., 2023a), GaussianDreamer (Yi et al.,
2023) and (Chen et al., 2023c) apply Gaussian splatting to the SDS loss.

#### Methods using multi-view generation.

Many methods have proposed to use multi-view generation to improve 3D
generation. For multi-view generation, the most common approach is Zero-1-to-3
(Liu et al., 2023b), which fine-tunes the Stable Diffusion (SD) model to
generate novel views of an object. Zero123++ (Shi et al., 2023a) further
improves on this base model in various ways, including generating directly a
grid of several multi-view images. Cascade-Zero123 (Chen et al., 2023b)
proposes to apply two such models in sequence: the first to obtain approximate
multiple views of the object, and the second to achieve better quality views
conditioned on the approximate ones.

Magic123 (Qian et al., 2023) and DreamCraft3D (Sun et al., 2023) combine
Zero-1-to-3 and SD. They start from a generated 2D image, extract depth and
normals, and apply the RealFusion / DreamBooth technique to fine-tune the 2D
diffusion model to generate different views of the object.

MVDream (Shi et al., 2023b) directly generates four fixed viewpoints of an
object from a text prompt. Consistent123 (Weng et al., 2023) uses a different
form of cross-view attention and generates several views sufficient for direct
reconstruction. ConsistNet (Yang et al., 2023) introduces an explicit 3D
pooling mechanism to exchange information between views. ImageDream (Wang &
Shi, 2023) extends MVDream to start from a given input image, and proposes a
new variant of image conditioning compared to that of Zero-1-to-3. RichDreamer
(Qiu et al., 2023) further learns to generate normals and separation between
material and lighting.

Viewset Diffusion (Szymanowicz et al., 2023), Forward Diffusion (Tewari et
al., 2023), SyncDreamer (Liu et al., 2023c) and DMV3D (Xu et al., 2023)
denoise multiple views of the 3D object simultaneously to improve consistency.

3DGen (Gupta et al., 2023) learns a latent space to encode 3D objects using a
VAE-like technique. The latent space is then used by a diffusion model that
draws samples from it. However, this approach is not very scalable as it
requires training the model from scratch using 3D data. HexaGen3D (Mercier et
al., 2024) extends 3DGen to use features from an SD model instead, thus
increasing the scalability of the approach. A concurrent work is ViVid-1-to-3
(Kwak et al., 2023), which also uses a video generator for multi-view
generation, but does not produce any 3D assets (only novel views).

#### Non-SDS methods.

Some text-to-3D methods perform “direct” 3D reconstruction on top of generated
views without using SDS. One–2–3–45 (Liu et al., 2023a) compensates for the
shortcomings of the multi-view generator by training a reconstruction network.
Instant3D (Li et al., 2023) is similar, but based on a much larger
reconstruction model (Hong et al., 2023). Wonder3D (Long et al., 2023) further
learns to generate multiple views of a given input image together with the
corresponding normal maps, which are then used to reconstruct the 3D object.
AGG (Xu et al., 2024) builds a single-image reconstruction network on top of
Gaussian splatting. CAD (Wan et al., 2023) learns a 3D generator network from
image samples using a 2D diffusion model, replacing the SDS loss with
adversarial training.

Our approach also eschews the SDS loss, but shows that it is possible to
offload most of the modelling burden to the 2D generator network, utilizing a
straightforward and efficient 3D reconstruction algorithm.

##  3 Method

Figure 2: Overview of IM-3D. Our model starts from an input image (e.g.,
generated from a T2I model). It feeds the latter into an image-to-video
diffusion model to generate a turn-table like video. The latter is plugged
into 3D Gaussian Splatting to _directly_ reconstruct the 3D object using
image-based losses for robustness. Optionally, renders of the objects are
generated and fed back to the video diffusion model, repeating the process for
refinement.

We first describe our video-based multi-view generator network in Section 3.1
and its training data in Section 3.2, followed by a description of the robust
3D reconstruction module in Section 3.3 and of iterative refinement in Section
3.4. An overview of our method is shown in Figure 2.

###  3.1 Multi-view as video generation

Our multi-view generation model is based on fine-tuning an existing text-to-
video (T2V) generator network Emu Video (Girdhar et al., 2023). First, it
utilizes a text-to-image (T2I) model (Emu (Dai et al., 2023)) to generate an
initial image $\mathbf{I}$ corresponding to the given textual prompt
$\boldsymbol{p}$. Second, the image $\mathbf{I}\in\mathbb{R}^{3\times H\times
W}$ and the text prompt $\boldsymbol{p}$ are fed into a second generator,
which produces up to $K=16$ frames of video $\mathbf{J}\in\mathbb{R}^{K\times
3\times H\times W}$, utilizing $\mathbf{I}$ as guidance for the first frame.
Notice that, while the model is trained such that
$\mathbf{I}\approx\mathbf{J}_{1}$, this is not an exact equality. Instead, the
model draws a sample $\mathbf{J}$ from a learned conditional distribution
$p(\mathbf{J}|\mathbf{I},\boldsymbol{p})$, which allows it to slightly deviate
from the input image to better fit in the generated video. An advantage of Emu
Video compared to other video generators is that the video frames $\mathbf{J}$
are already high-quality and high-resolution, without requiring sophisticated
coarse-to-fine sampling schemes. It is architectured as a fine-tuned version
of the original T2I Emu network with some modifications to account for the
temporal dependencies between frames.

Starting from the pre-trained Emu Video model, we then fine-tune it to
generate a particular kind of video, where the camera moves around a given 3D
object, effectively generating _simultaneously_ several views of it, in a
turn-table-like fashion. In order to do so, we consider an internal dataset of
synthetic 3D objects, further described in Section 3.2. This dataset provides
us with training videos
$\mathcal{J}=\\{(\mathbf{J}_{n},\mathbf{I}_{n},\boldsymbol{p}_{n})\\}_{n=1}^{N}$,
each containing $K=16$ views of the object taken at fixed angular interval and
a random but fixed elevation, the initial image
$\mathbf{I}_{n}=[\mathbf{J}_{n}]_{1}$, and the textual prompt
$\boldsymbol{p}_{n}$. The camera distance is fixed across all renders.

Differently from many prior multi-view generation networks, we _do not_ pass
the camera parameters to the model; instead, we use a fixed camera distance
and orientation, randomizing only the elevation. The model simply learns to
produce a set of views that follow this distribution.

Like most image and video generators, Emu Video is based on _diffusion_ and
implements a denoising neural network
$\hat{\epsilon}(\mathbf{J}_{t},t,\mathbf{I},\boldsymbol{p})$ that takes as
input a noised video
$\mathbf{J}_{t}=\sqrt{1-\sigma_{t}^{2}}\mathbf{J}+\sigma_{t}\epsilon,$ where
$\epsilon\sim N(0,I)$ is Gaussian noise and $\sigma_{t}\in[0,1]$ is the noise
level, and tries to estimate the noise $\epsilon$ from it. The training uses
the standard diffusion loss
$\mathcal{L}_{\text{diff}}(\hat{\epsilon}|\mathbf{J},\mathbf{I},\boldsymbol{p},t,\epsilon)=w^{\text{diff}}_{t}\cdot\|\hat{\epsilon}(\mathbf{J}_{t},t,\mathbf{I},\boldsymbol{p})-\epsilon\|^{2}$
where $(\mathbf{J},\mathbf{I},\boldsymbol{p})\in\mathcal{J}$ is a training
video, $\epsilon$ is a Gaussian sample, $t$ is a time step, also randomly
sampled, and $w_{t}$ is a corresponding weighing factor. To finetune Emu
Video, we use $\mathcal{L}_{\text{diff}}$, but freeze all parameters except
for the temporal convolutional and attention layers.

###  3.2 Data

The dataset $\mathcal{J}$ used to train our model consists of turn-table-like
videos of synthetic 3D objects. Several related papers in multi-view
generation also use synthetic data, taking Objaverse (Deitke et al., 2022) or
Objaverse-XL (Deitke et al., 2023) as a source. Here, we utilize an in-house
collection of 3D assets of comparable quality, for which we generate textual
descriptions using an image captioning network.

Similar to prior works (Li et al., 2023), we use a subset of 100k assets
selected for quality, as determined by the CLIP (Radford et al., 2021)
alignment between rendered images and textual descriptions. Each video
$\mathbf{J}\in\mathcal{J}$ is obtained by sampling one of the 100k assets,
choosing a random elevation in $[0,\pi/4]$, and then placing the camera around
the object at uniform ($2\pi/K$ degree) intervals.

###  3.3 Fast and robust reconstruction

To generate a 3D asset from a prompt $\boldsymbol{p}$, we first sample an
image $\mathbf{I}\sim p(\mathbf{I}|\boldsymbol{p})$ from the Emu image model,
followed by sampling a multi-view video $\mathbf{J}\sim
p(\mathbf{J}|\mathbf{I},\boldsymbol{p})$ from the fine-tuned Emu Video model.
Given the video $\mathbf{J}$, we then _directly_ fit a 3D model $G$. While
there are many possible choices for this model, here we use Gaussian splatting
(Kerbl et al., 2023), a radiance field that uses a large number of 3D
Gaussians to approximate the 3D opacity and color functions.

Given the 3D model $G$ and a camera viewpoint $\Pi$, the _differentiable_
Gaussian splatting renderer produces an image
$\hat{\mathbf{I}}=\mathcal{R}(G,\Pi)$. Compared to other methods such as NeRF
(Mildenhall et al., 2020), or even faster versions such as DVGO (Sun et al.,
2022) or TensoRF (Chen et al., 2022), the key advantage of Gaussian splatting
is the efficiency of the differentiable renderer, both in time and space,
which allows rendering a _full_ high-resolution image $\mathbf{I}$ at each
training iteration instead of just selected pixels as in most prior works.
Because of this fact, we can utilize _image-level_ losses such as LPIPS (Zhang
et al., 2018), i.e.,
$\mathcal{L}_{\text{LPIPS}}(\hat{\mathbf{I}},\mathbf{I})=\sum_{q=1}^{Q}\|w_{q}\odot(\Phi_{q}(\hat{\mathbf{I}})-\Phi_{q}(\mathbf{I}))\|^{2}$
where $\Phi_{q}:\mathbb{R}^{3\times H\times W}\rightarrow\mathbb{R}^{C}$ is a
family of $Q$ patch-wise feature extractors implemented by the VGG-VD neural
network (Simonyan & Zisserman, 2015). We also utilize a second image-based
loss $\mathcal{L}_{\text{MS-SSIM}}$, the multi-scale structural similarity
index measure (MS-SSIM) (Wang et al., 2003). Finally, we use a mask loss
$\mathcal{L}_{\text{Mask}}$ with masks obtained using the method introduced in
(Qin et al., 2022). In our ablation studies, we show the significant benefits
of using these image-based losses rather than the standard pixel-wise RGB loss
$\mathcal{L}_{\text{RGB}}(\hat{\mathbf{I}},\mathbf{I})=\|\hat{\mathbf{I}}-\mathbf{I}\|^{2}$.
Our final loss is the weighted loss combination
$\mathcal{L}=w_{\text{LPIPS}}\mathcal{L}_{\text{LPIPS}}+w_{\text{SSIM}}\mathcal{L}_{\text{SSIM}}+w_{\text{Mask}}\mathcal{L}_{\text{Mask}}.$
The object $G$ is thus reconstructed via direct optimization, i.e.,
$G^{*}=\operatornamewithlimits{argmin}_{G}\sum_{k=1}^{K}\mathcal{L}(\mathcal{R}(G,\Pi_{k}),[\mathbf{J}]_{k})$
where $[\mathbf{J}]_{k}$ denotes the $k$-th image in the video.

Table 1: Faithfulness to the textual and visual prompts of image sequences synthesised or rendered by various methods. Assessed on the prompt list from (Wang & Shi, 2023; Shi et al., 2023b). |  | synthesized view | re-rendered view
---|---|---|---
model |  | Time (min)
---
CLIP (Text) | CLIP (Image) | CLIP (Text) | CLIP (Image)
SDXL (Podell et al., 2023) [upper bound] | 0.03 | 33.33 | 100 | — | —
MVDream (Shi et al., 2023b) | 72 | 31.26 ±2.9 | 76.44 ±6.5 | 30.63 ±2.7 | 76.94 ±5.2
Zero123XL (Deitke et al., 2023) | 10 | 19.58 ±1.3 | 60.29 ±5.8 | 29.06 ±3.3 | 81.33 ±6.9
Magic123 (Qian et al., 2023) | 15 | — | — | 29.51 ±4.7 | 84.14 ±10.2
SyncDreamer (Liu et al., 2023c) | 13 | 27.76 ±3.0 | 77.26 ±7.2 | 26.22 ±3.4 | 74.95 ±6.6
ImageDream (Wang & Shi, 2023) | 120 | 31.08 ±3.4 | 85.39 ±5.8 | 30.73 ±2.3 | 83.77 ±5.2
OpenLRM (Hong et al., 2023) | 0.17 | — | — | 29.75 ±3.2 | 83.08 ±9.5
One2345++ (Liu et al., 2023a) | 0.75 | — | — | 29.71 ±2.3 | 83.78 ±6.4
IM-3D (ours) | 3 |  31.92 ±1.6 |  92.38 ±5.1 |  31.66 ±1.7 |  91.40 ±5.5

###  3.4 Fast sampling and iterative generation

The SDS loss can be seen as a way to bridge the gap between image generators
that are unaware of 3D objects and their 3D reconstructions, absorbing multi-
view consistency defects in the generation. Because our model is rather view-
consistent from the outset, and because we can use robust reconstruction
losses, the SDS loss is unnecessary. Instead, given a prompt $\boldsymbol{p}$,
we simply generate an image $\mathbf{I}$, followed by video $\mathbf{J}$, and
then fit a 3D object $G$ to the latter.

One main advantage is that this _dramatically_ reduces the number of model
evaluations compared to using the SDS loss. Optimizing the SDS loss is
(approximately) the same as ascending the score, i.e., the gradient
$\nabla\log p(\mathbf{J}_{t}|\mathbf{I},\boldsymbol{p})$ of the log
distribution over noised videos (or images), so the optimization of the asset
$G$ can be seen as a form of multi-view mode seeking. The score is obtained
from the same network $\hat{\epsilon}$. However, despite the conditioning on a
specific textual prompt $\boldsymbol{p}$ and input view $\mathbf{I}$, the
sampled distribution is rather wide, requiring a very large number (thousands)
of iterations to converge to a mode; furthermore, regressing to a mode reduces
the diversity and quality of the output.

In our case, the network $\hat{\epsilon}$ is used to generate directly a
_single_ video $\mathbf{J}$, which is then reconstructed without further
invocations to the model. Because the video $\mathbf{J}$ is already
sufficiently view-consistent, the 3D reconstruction converges quickly to a
good solution. Furthermore, we can adopt fast stochastic ODE solvers such as
DPM++ (Lu et al., 2022) to further reduce the number of model evaluations to
obtain the video in the first place. Overall, compared to using the SDS loss,
the number of model evaluations is reduced by a factor $10$-$100\times$ (see
the Appendix for additional analysis).

Despite the overall consistency of generated videos $\mathbf{J}$, they are
still not perfect. We thus additionally compensate for such inconsistencies
during model fitting, but still without resorting to the SDS loss. Instead, we
alternate 3D reconstruction and video generation. To do so, once the first
video $\mathbf{J}$ and corresponding 3D model $G^{*}$ are obtained, we use the
latter to generate a video $\mathbf{J}^{*}=\mathcal{R}(G^{*},\Pi)$ using the
3D renderer, sample an intermediated noised video $\mathbf{J}^{*}_{t}$ by
adding noise to it as shown above, and then invoking the video generator again
to obtain a denoised and updated video $\mathbf{J}^{\prime}$.

We iterate this process two times. This is vastly faster than using the SDS
loss while still being highly robust.

Figure 3: Qualitative Comparisons. Our method IM-3D (last row) and others for
the same text/image prompt pairs. For IM-3D, we show the final GS
reconstruction (which guarantees multi-view consistency). We match the input
image faithfully and obtain high-quality, detailed reconstructions in just 3
minutes. Faster methods such as OpenLRM are also much worse.  Figure 4: A
visualization of reconstruction quality over multiple iterations of multiview
diffusion and reconstruction. We compare the initial reconstructions obtained
by our model (i.e. the result of training on our initial generated videos) to
the result after one iteration of reconstruction and refinement. We see that
although the initial reconstructions have reasonable shapes, they lack fine-
grained details due to small inconsistencies in the generated multiview
images. After one iteration of noising, denoising, and reconstruction, our
method resolves these inconsistencies and produces 3D assets with
significantly higher levels of detail (as highlighted by the red circles
above).  Figure 5: Human evaluation. We perform human evaluation of IM-3D vs
state-of-the-art in Image-to-3D and Text-to-3D. Human raters preferred IM-3D
to all competitors with regard to both generation quality and faithfulness,
often by a large margin.

##  4 Experiments

Our method generates 3D objects from a textual description $\boldsymbol{p}$
and a reference image $\mathbf{I}$. In order to compare to prior work, we
consider in particular the set of textual prompts from (Shi et al., 2023b),
which are often used for evaluation.

Given an input image and prompt $(\mathbf{I},\boldsymbol{p})$, previous
methods either _synthesize_ a multi-view image sequence $\mathbf{J}$ (usually
by means of a generator network), or output a 3D model, or both. We compare
the quality of the produced artifacts visually, utilizing the image sequence
$\mathbf{J}$ directly, or corresponding _renders_ $\hat{\mathbf{J}}$ of the 3D
model. In general, we can expect the quality and faithfulness of $\mathbf{J}$
to be better than that of $\hat{\mathbf{J}}$ because the generated image
sequence needs not be perfectly view-consistent. On the other hand, the
renders $\hat{\mathbf{J}}$ from the 3D model are consistent by construction,
but may be blurrier than $\mathbf{J}$, or contain other defects.

###  4.1 Comparison to the state-of-the-art

In this section, we compare IM-3D to relevant state-of-the-art methods in the
literature, including MVDream (Shi et al., 2023b), Zero123XL (Deitke et al.,
2023), Magic123 (Qian et al., 2023), SyncDreamer (Liu et al., 2023c),
ImageDream (Wang & Shi, 2023), LRM (Hong et al., 2023) and One2345++ (Liu et
al., 2023a). For LRM, since no public models are available, we utilize the
open-source OpenLRM (He & Wang, 2023). For One2345++, which is only available
via a web interface, we manually upload each image in the evaluation set. We
carry out both quantitative and qualitative comparisons using the set of
prompts and images from (Shi et al., 2023b; Wang & Shi, 2023).

#### Quantitative comparison.

Table 1 provides a quantitative comparison of our method to others. We adopt
the same metrics as (Shi et al., 2023b; Wang & Shi, 2023), which are based on
the CLIP (Radford et al., 2021) similarity scores. Specifically, we utilize
the ability of CLIP to embed text and images in the same space. We then use
the embeddings to compare the textual prompt $\boldsymbol{p}$ and the image
prompt $\mathbf{I}$ to the images $\mathbf{J}$ of the object (either
synthesized or rendered). A high CLIP similarity score means high faithfulness
to the prompt. As an upper bound, we also report the CLIP scores of the prompt
images $\mathbf{I}$ which were generated using the SDXL (Podell et al., 2023)
model.

The key takeaway from Table 1 is that IM-3D outperforms all others in terms of
both textual and visual faithfulness. This is true for both the image
sequences $\mathbf{J}$ output by the video generator as well as the renders
$\hat{\mathbf{J}}$ from the fitted 3D GS models $G$. IM-3D is particularly
strong when it comes to visual faithfulness, which also means that the images
we generate are of a quality comparable to the input image $\mathbf{I}$.
Additionally, our method requires significantly less time than most (3 minutes
vs hours for some models).

#### Human evaluation.

Automated metrics for the evaluation of generative models are not fully
representative of value of the output in applications. Thus, we also conduct a
human study. We ask annotators to evaluate our model against a competitor
based on (1) Image Alignment and (2) 3D quality. We present to annotators with
the outputs of two different methods, rendered as 360° videos, and ask them to
indicate a preference based on these two criteria. Table 1 shows the win rate
when comparing our methods against others. Our method surpasses in performance
all other baselines in both studies indicating that the proposed method
produces high-quality 3D results that closely align with the image prompt.
Further details are provided in the Appendix.

Figure 6: Reconstruction Quality with Pixel-Level and Image-Level Losses. We
find that image-level losses are crucial to the success of our method. With
pixel-level losses such as the L2 loss, small inconsistencies in the generated
images are effectively averaged together, resulting in unnatural and blurry-
looking reconstructions.

###  4.2 Ablations

Table 2: Ablation on the importance of loss terms and 3D representation during the fitting stage. Loss / Representation | CLIP (Text) | CLIP (Image)
---|---|---
IM-3D (ours) |  31.66 ±1.7 |  91.40 ±5.5
\- $\mathcal{L}_{\text{LPIPS}}$ | 29.38 ±2.1 | 84.71 ±6.4
\- $\mathcal{L}_{\text{RGB}}$ instead of $\mathcal{L}_{\text{LPIPS}}$ | 29.67 ±2.0 | 84.99 ±5.9
\- $\mathcal{L}_{\text{SSIM}}$ | 31.53 ±1.8 | 90.64 ±5.7
\- $\mathcal{L}_{\text{Mask}}$ | 31.43 ±1.9 | 90.14 ±6.0
w/ NeRF instead of GS | 30.42 ±2.1 | 87.37 ±5.4

#### Effect of iterative refinements.

In Figure 4, we demonstrate the efficacy of our proposed iterative refinement
process. Our model’s initial reconstructions (derived from training on our
initially generated videos) are compared to the outcome following a single
iteration of multiview diffusion and reconstruction. While the initial
reconstructions exhibit satisfactory shapes, they miss out on intricate
details due to minor inconsistencies in the initial multiview images. In a few
instances, some parts of these initial reconstructions look as if two copies
of a shape have been superimposed upon one another, as the reconstruction
process tries to satisfy two inconsistent views. However, our technique
rectifies these discrepancies with one iteration of denoising and
reconstruction; significantly enhancing the level of detail.

#### Image-Level Losses.

In Table 2 and Figure 6, we compare results of optimization with pixel-level
and image-level loss functions. We find that image-level losses are central to
our method’s ability to generate high-quality 3D assets. The use of pixel-
level losses such as L2 loss is detrimental, as minor inconsistencies in the
multiview images are emphasized by the optimization process and effectively
averaged together. This averaging results in a low CLIP score (29.67 vs 31.66
for LPIPS) as well as blurry and unnatural generations.

#### Comparing 3D Representations

The last line of Table 2 provides a comparison of 3D representations, showing
the effect of using NeRF as an underlying 3D representation rather than
Gaussian splatting (GS). We find that the visual quality of models generated
using NeRF is slightly worse than GS. The true benefit of GS is that it is
much faster and much more memory-efficient; training with GS takes 3 minutes
whereas training with NeRF takes 40 minutes. Additionally, the memory-
efficient nature of Gaussian splatting makes it easy to render at our
diffusion model’s native resolution of $512$px, whereas for NeRF one has to
use ray microbatching or optimize at a lower resolution.

Table 3: Ablation on Using Fewer Frames. We show quantitative performance when performing our reconstruction and generation using fewer frames.  # Frames | CLIP (Text) | CLIP (Image)
---|---|---
16 |  31.66 ±1.7 |  91.40 ±5.5
8 | 31.38 ±1.8 | 90.06 ±6.3
4 | 30.06 ±2.6 | 86.96 ±8.6

#### Using Fewer Frames

Differently from the vast majority of other diffusion-based text-to-3D and
image-to-3D approaches, which generate only 1-4 frames, IM-3D generates 16
frames simultaneously. We demonstrate the significance of this in Table 3,
finding that our quantitative performance improves as we increase the number
of generated frames.

###  4.3 Limitations

The fine-tuned video generator is generally very view-consistent, but it still
has limitations. One interesting failure case is that for highly dynamic
subjects (e.g., horses, which are often captured running), the model sometimes
renders spurious animations (e.g., walking or galloping) despite our fine-
tuning, which is problematic for 3D reconstruction. This occurs more often
when the prompt contains verbs describing motion; see the Appendix for an
example.

##  5 Conclusions

In this work, we have shown that starting from a video generator network
instead of an image generator can result in better multi-view generation, to a
point where it can impact the design of future text-to-3D models. In fact, we
have shown that the quality is sufficient to eschew distillation losses like
SDS as well as large reconstruction networks. Instead, one can simply fit the
3D object to the generated views using a robust image-based loss.
Reconstruction can be further alternated with refining the target video,
quickly converging to a better 3D object with minimal impact on efficiency.
Compared to works that rely on SDS, our approach significantly reduces the
number of evaluations of the 2D generator network, resulting in a faster and
more memory-efficient pipeline without compromising on quality.

##  6 Impact Statement

Our work uses Generative AI, whose potential impacts are and have been
extensively discussed in the academic, business and public spheres. Our work
does not change these issues qualitatively. The Emu models (Dai et al., 2023)
were explicitly designed with fairness and safety in mind, and fine-tuning
them on curated 3D models is likely to further reduce the potential for harm.

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##  Appendix A Appendix

###  A.1 Training details

In line with (Girdhar et al., 2023), we maintain the spatial convolutional and
attention layers of Emu Video, fine-tuning only the temporal layers. We
minimize the standard diffusion loss over a span of 5 days, employing 80 A100
GPUs with a total batch size of 240 and a learning rate of 1e-5. Our findings
indicate that prolonged training effectively counters the network’s
inclination to generate 360 videos of deforming objects, given that the
initialization is a video generation model. Contrary to MVDream (Shi et al.,
2023b) and Instant3D (Li et al., 2023), we observe no degradation in texture
quality with extended training. This can be ascribed to the fact that the
spatial layers remain static and the network is image-conditioned,
necessitating that the generated 360 video retain the high-frequency texture
elements of the input.

For Gaussian fitting, we initialize 5000 points at the center of the 3D space,
and densify and prune the Gaussians every 50 iterations. We conduct
optimization for 1200 iterations and execute Emu Video twice for 10 iterations
each using the DPM solver (Lu et al., 2022) during this process, repeating
this every 500 iterations. Empirically, we found that setting the weights to
$w_{\text{LPIPS}}=10$, $w_{\text{SSIM}}=0.2$ and $w_{\text{Mask}}=1$ yields
the best results during the fitting stage.

Prior to fitting, we need to estimate the elevation of the very first
generated video. To that end, we trained an elevation estimator on top of DINO
(Caron et al., 2021) features using the 100k 3D renderings. The network
averages the features of 4 uniformly distributed frames and uses a 2-layer MLP
to regress the elevation in radians.

###  A.2 Human evaluation

In our study, we employed the prompt set delineated in (Shi et al., 2023b) to
conduct a human annotation evaluation via Amazon Mechanical Turk (AMT). The
task assigned to the annotators involved choosing between two 3D assets, both
rendered as 360° videos, with one of the assets being the output of our
proposed method. To ensure a robust and unbiased evaluation, we randomized the
presentation order of the methods for each question. Each question was
assessed by five annotators, and we reported the consensus opinion. Since each
question corresponds to a triplet of (competing method, 3D reconstruction, and
quality/faithfulness), this is a total of $5\text{ annotators}\times(5\text{
methods}\times 39\text{ reconstructions}\times 2\text{ question types})=1950$
annotations. We instructed the annotators to overlook any disparities in
background colors, as normalizing all methods to yield the same scale is a
non-trivial task.

###  A.3 Further information on network efficiency

| ProlificDreamer | MVDream | ImageDream | Zero123XL | SyncDreamer | IM-3D
---|---|---|---|---|---|---
| Number of
---
network calls
320000 | 20000 | 25000 | 1200 | 200 | 80
Table 4: Number of diffusion network calls to generate one 3D asset. The
proposed method, IM-3D, requires only a fraction of the model evaluations to
compute a 3D asset.

In Table 4, we present the number of diffusion model forward passes used to
reconstruct a single 3D object for various 3D generation methods. Whereas some
other methods require thousands or tens of thousands of iterations, our method
requires less than one hundred.

###  A.4 Failure Cases

Figure 7: Visualisation of a failure case. In this case, our finetuned video
network generated an animated video of a horse walking rather than a static
video. As a result, the 3D reconstruction process produces erroneous geometry
(e.g. the head of the horse is barely visible from some views).

As described in the limitations section of the main paper, the video generator
does not produce perfect results in all cases. A notable instance of failure
is observed with subjects that exhibit high dynamism, such as horses often
depicted in motion. In such cases, the model occasionally produces unwarranted
animations like walking or galloping, which disrupts the 3D reconstruction
process. We show an example in Figure 7.

###  A.5 Conversion to Meshes

Although Gaussian Splatting is being rapidly adopted by the computer vision
and graphics communities, some production applications require that objects be
converted to meshes. We show that in these cases, it is straightforward to
extract high-quality meshes from our Gaussian Splatting representation: one
can run marching cubes (Cline, 1988) and then optionally continue to optimize
the resulting mesh using DMTet (Shen et al., 2021). We show visual results of
converting our models to meshes in Figure 8.

Figure 8: Visualisation of Meshes. We convert our Gaussian Splatting
representation to DMTet (Shen et al., 2021) using marching cubes and optimize
the resulting meshes using our iterative multiview diffusion and
reconstruction process.


# Dead or Alive? How Bursty Star Formation and
Patchy Dust Can Cause Temporary Quiescence in High Redshift Galaxies

Andreas L. Faisst Caltech/IPAC, 1200 E. California Blvd. Pasadena, CA 91125,
USA  Takahiro Morishita Caltech/IPAC, 1200 E. California Blvd. Pasadena, CA
91125, USA

###### Abstract

The recent discovery of a galaxy at $z=7.3$ with undetected optical emission
lines and a blue UV to optical continuum ratio in JWST spectroscopy is
surprising and needs to be explained physically. Here, we explore two
possibilities that could cause such a seemingly quiescent $\sim 5\times
10^{8}\,{\rm M_{\odot}}$ galaxy in the early Universe: (i) stochastic
variations in the star formation history (SFH) and (ii) the effect of
spatially varying dust attenuation on the measured line and continuum emission
properties. Both scenarios can play at the same time to amplify the effect. A
stochastic star formation model (similar to realistic SFHs from hydrodynamical
simulations of similar-mass galaxies) can create such observed properties if
star formation is fast-varying with a correlation time of $<200\,{\rm Myrs}$
given a reasonable burst amplitude of $\sim 0.6\,{\rm dex}$. The total time
spent in this state is less than $20\,{\rm Myrs}$, and the likelihood of such
a state to occur over $500\,{\rm Myrs}$ at $z=7$ is $\sim 50\%$. On the other
hand, we show that a spectrum with blue UV continuum and lack of emission
lines can be reproduced by a blue$+$red composite spectrum. The UV continuum
is emitted from dust-free density bounded H II regions (blue component), while
the red component is a dust-obscured starburst with weakened emission lines
due to strong differential dust attenuation between stellar and nebular
emission. Future resolving far-infrared observations with ALMA will shed light
on the latter scenario.

High-redshift galaxies (734) – Galaxy quenching (2040) – Post-starburst
galaxies (2176) – Galaxy evolution (594)

††facilities: JWST††software:  astropy (Astropy Collaboration et al., 2013,
2018); DELightcurveSimulation (Connolly, 2016); FSPS (Conroy et al., 2009;
Conroy & Gunn, 2010); Galfit (Peng et al., 2010a); Python-FSPS (Johnson et
al., 2023)

##  1 Introduction

Galaxies spend a significant amount of their lives on the star forming main-
sequence (Noeske et al., 2007; Daddi et al., 2007), a tight $\sim 0.3\,{\rm
dex}$ relation between stellar mass and star formation rate (SFR). This
relation is maintained by an equilibrium between gas inflow, gas consumption
through star formation, and gas removal through outflow (e.g., stellar winds,
Supernovae, or AGN), as explained by simple analytical models (e.g., Davé et
al., 2012; Lilly et al., 2013; Feldmann, 2015). After the peak of cosmic star
formation density at $z\sim 2$ (e.g., Madau & Dickinson, 2014) a large number
of quiescent galaxies is observed, which deviate from the star forming main-
sequence by a significantly reduced star formation (e.g., Ilbert et al., 2010;
Abraham et al., 2007; Bell et al., 2004). Quiescent galaxies are mainly
characterized by their red colors and strong Balmer breaks due to old stellar
populations as well as the lack of emission lines signaling the lack of recent
star formation. The probability of being quiescent depends on the environment
and stellar (halo) mass of a galaxy (e.g., Peng et al., 2010b). Quenching can
happen through many avenues, for example by gas shock-heating in the most
massive dark matter halos (common at high masses; Croton et al., 2006;
Cattaneo et al., 2008) and interactions with starbursts or feedback from AGN
and Supernovae (also common at lower masses; Toomre & Toomre, 1972; Haas et
al., 2013; Schawinski et al., 2014).

While the quiescent galaxy population is established at $z<2$, there are open
questions regarding their progenitors and when and how the first quiescent
galaxies emerge. With the advent of sensitive infrared detectors and
spectrographs, it became feasible to spectroscopically detect and confirm
quiescent galaxies out to $z\sim 4$ (Glazebrook et al., 2017; Schreiber et
al., 2018; Tanaka et al., 2019; Forrest et al., 2020; Valentino et al., 2020;
D’Eugenio et al., 2021; Kubo et al., 2021; Nanayakkara et al., 2022; Carnall
et al., 2023a, b). Most of these findings are massive galaxies and are thought
to be quiescent for good. However, the fate of less massive quiescent galaxies
is unclear because their star formation is suggested to significantly
oscillate around the equilibrium state (i.e. the main sequence) due to the
continuous adjusting between gas supply and consumption, hence leading to
“temporary quiescence” (e.g., Tacchella et al., 2016).

With the James Webb Space Telescope (JWST) in operation, yet another leap
forward has been achieved regarding the study of high-redshift quiescent
galaxies (see Valentino et al., 2023). Specifically, Looser et al. (2023a)
reported a seemingly quiescent galaxy (in the following JADES-GS-z7-01-QU) at
a spectroscopic redshift of $z=7.3$. The galaxy is curious as it is lacking
optical emission lines and at the same time shows a blue UV to optical flux
ratio in the present JWST/NIRSpec spectra. A quiescent galaxy at such a high
redshift (where commonly a significant amount of molecular gas is available)
may be surprising. “Mass quenching” is likely not applicable due to the lower
masses of dark-matter halos at those epochs, instead feedback or variations in
the star formation history (SFH) of the galaxy are more likely. The lack of
optical line emission suggests the lack star formation in the past $\sim
10\,{\rm Myrs}$, indicative of a short-term variation in the SFH of this
galaxy (e.g., Kennicutt, 1998). Furthermore, the galaxy shows a blue UV to
optical flux ratio of $\sim 3$ (in $\lambda f_{\rm\lambda}$), indicative of
young stars and/or low stellar continuum dust obscuration. Since the galaxy is
at a relatively low stellar mass ($\sim 5\times 10^{8}\,{\rm M_{\odot}}$), the
most likely scenarios involve temporal quenching by star formation or AGN
feedback (e.g., Gelli et al., 2023; Lovell et al., 2023) or galaxy-galaxy
interactions (Asada et al., 2023). A recent study found that the most dominant
quenching mechanisms for low-mass high-redshift galaxies may be accretion disk
feedback triggered by disk instabilities (Xie et al., 2024). Alternatively, an
unlucky geometric distribution of dust along the line of sight could attenuate
the nebular emission and mimic quiescence. In the mean time, similar galaxies
have been found at $z\sim 4.5$ (Looser et al., 2023b), in agreement with the
bursty star formation expected at these redshifts (e.g., Faisst et al., 2019).

Figure 1: Left: UV-to-optical flux ratio (blue) and [O III] SNR (red) as a
function of time for a simple burst model with $10\,{\rm M_{\odot}\,yr^{-1}}$
over $50\,{\rm Myrs}$ (hatched area). The observed values for JADES-
GS-z7-01-QU are indicated as blue and red horizontal filled areas,
respectively. Right: UVJ diagram showing the observe colors of JADES-
GS-z7-01-QU (square) and different models. The simplistic burst$+$passive
evolution model with maximal age of $700\,{\rm Myrs}$ is shown as thick line
with squares colored by the [O III] SNR (proportional to the [O III]
equivalent width). The colored points show a stochastic model ($\tau_{\rm
break}=100\,{\rm Myrs}$, $\sigma_{\rm MS}=0.6\,{\rm dex}$) over $700\,{\rm
Myrs}$. The gray points show a stochastic model with $\tau_{\rm
break}=700\,{\rm Myrs}$ and $\sigma_{\rm MS}=0.3\,{\rm dex}$ resulting in less
scatter. Note that dust changes the colors long the separation line between
star-forming and quiescence (gray area).

Several studies tried to explain the nature of JADES-GS-z7-01-QU, specifically
the combination of a blue UV to optical flux ratio and the absence of optical
emission lines. Simulations can explain quiescent galaxies and their number
densities in the Epoch of Reionization (e.g., Dome et al., 2023; Vikaeus et
al., 2023; Lovell et al., 2023; Gelli et al., 2023; Pallottini & Ferrara,
2023), and temporal quiescence is also expected given the burstiness of star
formation especially at lower stellar masses at high redshifts (e.g., Dome et
al., 2023; Sun et al., 2023a, b; Abramson & Kelson, 2020; Faisst et al., 2019;
Sparre et al., 2017; Kelson, 2014). However, the exact spectral energy
distribution (SED) of JADES-GS-z7-01-QU could not be reproduced so far (see,
e.g., Gelli et al., 2023).

In this study, we aim at explaining the observation of a galaxy such as JADES-
GS-z7-01-QU by two (not mutually exclusive) ideas: (i) the stochastic
variations in the SFH of high-$z$ galaxies (Section 2), and (ii) spatial
variations in the dust attenuation (Section 3). We assume a $\Lambda$CDM
cosmology with $H_{0}=70\,{\rm km\,s^{-1}\,Mpc^{-1}}$, $\Omega_{\Lambda}=0.7$,
and $\Omega_{\rm m}=0.3$. Magnitudes are given in the AB system (Oke, 1974),
and stellar masses and SFRs are normalized to a Chabrier (2003) initial mass
function (IMF).

##  2 Quiescence in Stochastic Star Formation Histories

In this section, we study the properties of JADES-GS-z7-01-QU under the
assumption that the galaxy follows a stochastic SFHs. We want to recover the
color properties as well as the lack of emission lines observed with JWST
NIRCam and NIRSpec data. Specifically, we observe two conditions (Looser et
al., 2023a):

#####  Condition 1.

A conservative non-detection of optical emission lines, specifically [O
II]λ3727 and [O III]λ5007 at no more than $3\sigma$ assuming a continuum
signal-to-noise ratio (SNR) of $5$.

#####  Condition 2.

A blue UV to optical $\lambda f_{\rm\lambda}$ flux ratio of $3\pm 0.3$ as
observed.

In the following, we show that the observed properties of JADES-GS-z7-01-QU
can be reproduced after a starburst. We then move on to estimate the
probability of such a scenario to happen within more complicated stochastic
representations of SFHs.

###  2.1 Single Starburst

The lack of optical emission lines is indicative of a lack of star formation
in the past $\sim 5-10\,{\rm Myrs}$. This condition can be reproduced by a
single starburst (Figure 1). Specifically, we assume a burst with a SFR of
$10\,{\rm M_{\odot}\,yr^{-1}}$ and a length of $50\,{\rm Myrs}$ (indicated by
the black hatched area). We assume that all mass is produced during that time,
while any previous star formation only has negligible impacts on the results
observed here. The blue and red lines on the left panel in Figure 1 show the
UV-to-optical flux ratio (condition 2) and the [O III] SNR (condition 1) in
this scenario, respectively. The observed values are indicated as blue and red
horizontal areas. Shortly ($\sim 5\,{\rm Myrs}$) after the end of the burst,
both conditions 1 and 2 are met. The right panel of Figure 1 shows the burst
model with subsequent passive evolution (line) on the UVJ diagram. The
corresponding [O III] SNR (proportional to the [O III] equivalent width) is
shown in color and it drops to zero after the burst. This diagram is commonly
used to separate star-forming from quiescent galaxies (e.g., Wuyts et al.,
2007). Assuming passive evolution, a galaxy would slowly evolve towards the
quiescent regime (here maximum at $t_{\rm Hubble}(z=7.3)\sim 700\,{\rm
Myrs}$). Note that dust attenuation moves colors to the upper right (red
arrow), parallel to the dividing line between star formation and quiescence,
hence this analysis is insensitive to the assumed dust attenuation. The
observed colors of JADES-GS-z7-01-QU are shown by the large square, and they
agree well with the burst$+$passive evolution model right at the end of the
burst.

The above analysis shows that the conditions of JADES-GS-z7-01-QU can be
explained right after a burst of star formation, and it assumes that the
galaxy stays quiescent afterwards. It is suggested that star formation can be
quenched for good in low-mass dark matter halos during the epoch of
reionization, giving rise to quiescent dwarf galaxies in the present days
(e.g., Bullock et al., 2000; Brown et al., 2014). However, realistically, the
SFH of galaxies at these masses is much more complex and a pure passive
evolution after a burst is unlikely given the large gas abundances at those
early times (however, see Xie et al. 2024 for theoretical evidence that most
early quiescent galaxies stay quiescent for $\sim 1\,{\rm Gyr}$). Dark matter
halo related quenching is not effective at low masses. The question of how
often such a scenario would occur in a more realistic stochastic SFH therefore
arises. This is explored in the next sections.

###  2.2 Stochastic Star Formation Histories

####  2.2.1 Synthetic spectra derived from stochastic SFHs

The SFH of galaxies can be characterized by stochastic processes depending on
feedback, inflow, and outflow of gas. This stochasticity will lead to
starbursts and short periods of quiescence, potentially giving rise to JADES-
GS-z7-01-QU as shown in Section 2.1. Here, we use an approach similar to
Caplar & Tacchella (2019) to create model SFHs with different statistical
properties. Specifically, we use the DELightcurveSimulation Python
package111https://github.com/samconnolly/DELightcurveSimulation (Connolly,
2016), which is based on the Emmanoulopoulos et al. (2013) light curve
simulation algorithm. In the following, we assume a generalized power spectrum
density (PSD) based on a damped random walk

${\rm PSD}(f)=\frac{\sigma^{2}}{1+(\tau_{\rm break}\,f)^{\alpha}},$ (1)

where $f$ is the frequency (i.e. inverse time in Myrs), $\tau_{\rm break}$ is
related to the decorrelation time scale of star formation over time, $\sigma$
is the long-term variability (or amplitude), and $\alpha$ describes the slope
of the PSD ($\alpha=2$ corresponds to a damped random walk). Note that in the
following $\sigma=\sigma_{\rm MS}$, the amplitude of star formation burstiness
with respect to the main-sequence in dex, is assumed. SFHs are created by
using the Simulate_TK_Lightcurve function using the Timmer & Koenig (1995)
method and the above parameterization of the PSD. Synthetic spectra are
derived using the Flexible Stellar Population Synthesis (FSPS; Conroy et al.,
2009; Conroy & Gunn, 2010) code222We are using the Python implementation,
https://dfm.io/python-fsps/ (Johnson et al., 2023). by feeding it the
stochastic SFHs obtained before. We enable emission lines (with $\log U=-2$),
assume a $10-50\%$ solar metallicity of gas and stars (the exact value does
not impact the following results), a Chabrier (2003) IMF, and “Padova 1994”
stellar evolutionary tracks (Bertelli et al., 1994) together with a Basel
Stellar Libary (BaSeL, version 3.1; Lejeune et al., 1997, 1998; Westera &
Buser, 2003) complemented by the empirical STELIB library (Le Borgne et al.,
2003).

####  2.2.2 How likely is it to observe JADES-GS-z7-01-QU?

For deriving a probability to observe a galaxy with similar properties as
JADES-GS-z7-01-QU within a given time frame, we create several 1000 synthetic
spectra for $\tau_{\rm break}~{}=~{}50,100,250,400,500\,{\rm Myrs}$ and
$\sigma~{}=~{}0.4,0.6,0.8\,{\rm dex}$. Furthermore, we fix $\alpha=2$ (see
Caplar & Tacchella 2019 for observational evidence) and the redshift is
assumed to be $z=7.3$. These spectra are then evaluated under the two
conditions outlined above. If both conditions are satisfied, a suitable analog
of JADES-GS-z7-01-QU has been found.

Figure 2: Top: Probability (in per-cent) of the occurrence of a galaxy with
condition 1 (no emission lines; hatched) or conditions $1+2$ (no emission
lines and blue UV to optical flux ratio; filled) within $500\,{\rm Myrs}$ at
$z=7.3$ for different main-sequence scatter amplitudes ($\sigma_{\rm MS}$) and
$\tau_{\rm break}$. The calculation is based on synthetic spectra derived for
stochastic SFHs (Section 2.2.1). Bottom: Time spend in either condition 1
(small empty circles) or conditions $1+2$ (large filled circles) for the same
selection of $\sigma_{\rm MS}$ and $\tau_{\rm break}$. The symbols are offset
in $x-$direction to improve visual clarity.

The number of $30\,{\rm Myr}$ time-slices that meet either condition 1 or both
conditions $1+2$ are then computed within a $500\,{\rm Myr}$ time window to
derive a probability of such a galaxy to occur over that time frame. The top
panel in Figure 2 summarizes this likelihood in per-cent for the different
$\tau_{\rm break}$ and $\sigma_{\rm MS}$. Generally, we find that the
likelihood to observe either condition increases with larger amplitude
($\sigma_{\rm MS}$), and decreases with increasing $\tau_{\rm break}$. This is
not surprising $-$ high amplitudes are necessary to produce a blue UV
continuum and short correlation timescales are needed to produce frequent fast
drops in star formation on the timescale of the de-excitation of nebular
emission lines (c.f. Section 2.1). Naturally, observing both a lack of
emission lines and a blue UV to optical flux ratio is significantly less
likely, which is the main difficulty in reconciling the observed properties of
JADES-GS-z7-01-QU (see Gelli et al., 2023). We note that in the case of this
analysis, $\tau_{\rm break}$ and $\sigma_{\rm MS}$ are degenerate. We can
break this degeneracy by fixing $\sigma_{\rm MS}$, i.e. the burstiness of the
star formation, which is a quantity that can be measured from observations.
Assuming a fiducial $\sigma_{\rm MS}=0.5-0.6\,{\rm dex}$ (which is likely a
good assumption given the measured burstiness of low-mass galaxies at high
redshifts, see Faisst et al., 2019), the occurrence of a galaxy with
properties similar to JADES-GS-z7-01-QU is most likely ($>50\%$) within
$500\,{\rm Myrs}$ for a considerably varying SFH at a rate of $\tau_{\rm
break}\lesssim 200\,{\rm Myrs}$.

The bottom panel of Figure 2 shows the estimated time a galaxy would spend on
average in either condition 1 or conditions $1+2$. This “quiescent time“
increases with $\tau_{\rm break}$ from $\sim 10\,{\rm Myrs}$ to over $50\,{\rm
Myrs}$ in the case of condition 1. Imposing conditions $1+2$, the average time
is $\sim 10\,{\rm Myrs}$ in all cases. Note that this is an extremely short
time scale in which the SFH variations must be aligned to produce the required
properties.

Finally, the right panel of Figure 1 shows two stochastic models with
different statistics on the UVJ diagram. More stochasticity (i.e. smaller
$\tau_{\rm break}$) results in larger scatter (color-coded points),
overlapping with the observed color of JADES-GS-z7-01-QU. Longer correlation
timescales results in a narrower parameter space (underlying gray points),
similarly to a more simple burst$+$passive evolution model.

Figure 3: Top: Recovery of $\tau_{\rm break}$ from simulated stochastic SFHs
for variable $\alpha$. Uncertainties are shown in different shades of blue.
Middle: Examples Sphinx20 SFHs from the $z=7$ slice across $500\,{\rm Myrs}$
normalized by the mean SFR (fit by 2-degree polynomial across the same time
span) and sorted by fitted $\tau_{\rm break}$. The “main-sequence” with $\pm
0.3\,{\rm dex}$ scatter is indicated by the horizontal gray-shaded region. The
galaxies are of similar stellar mass as JADES-GS-z7-01-QU. Bottom: Fraction of
the simulated galaxies going through observed conditions 1 (hatched) and both
conditions $1+2$ (solid) over their lifetime until $z=7$ (left). Histogram
(right) of corresponding average times of a galaxy spent in conditions 1
(black hatched) and $1+2$ (gray solid).

Figure 4: Left: RGB image of JADES-GS-z7-01-QU (MSA slit indicated). The image
is generated with the Galfit models fit to the JWST/NIRCam F115w, F210M, and
F410M images and convolved with the F115W PSF. There are indications of a blue
and red component suggestive of different dust obscuration. Right: Composite
spectra of a blue (density bounded) and red (older, dust-obscured) component.
The upper and lower panels show two different stellar continuum E(B-V) of the
red component. A differential dust attenuation factor of $0.44$ is assumed.
This shows that emission lines can be “absent” in the case of high dust
attenuation in compact starbursts, while the blue continuum is dominated by
the blue, dust-free young component of the galaxy (or galaxy merger). Even in
the case of dust attenuation, H$\alpha$ could be visible (however, outside of
the NIRSpec wavelength coverage, gray region).

####  2.2.3 Comparison to Simulations at $z=7$

In the following, we use more realistic SFH histories from simulations from
the newly released Sphinx20 cosmological radiation hydrodynamic simulations
(Katz et al., 2023), which is the largest volume simulation ($20^{3}$ co-
moving ${\rm Mpc^{3}}$ containing several thousands of star-forming galaxies
at $z=6$) in the Sphinx suite (Rosdahl et al., 2018, 2022). The simulation
models the large-scale process of cosmic reionization and the detailed physics
of a multi-phase interstellar medium (ISM), and is therefore ideally suited
for recovering the properties of galaxies at $z>6$. For more information on
the details of this simulation, we refer to Katz et al. (2023).

We first compare the more realistic SFHs of Sphinx20 to our stochastic models.
For this, we extract the SFHs of all $177$ galaxies (or halos) in the $z=7$
redshift slice. The galaxies have similar stellar masses as JADES-GS-z7-01-QU,
hence are ideal for comparing. The SFHs are then normalized to their mean SFR
(fit by a 2-degree polynomial) to obtain an estimate for $\sigma_{\rm MS}$.
The resulting normalized SFHs are then fit with the PSD model described in
Section 2 to obtain a value for $\tau_{\rm break}$. The best-fit parameters
for the $z=7$ Sphinx20 population are $\sigma_{\rm MS}=0.67\pm 0.04\,{\rm
dex}$, $\tau_{\rm break}=202\pm 117\,{\rm Myrs}$, and $\alpha=1.3\pm 0.2$. We
verified the fitting method by simulating several $100$ SFHs for various
$\tau_{\rm break}$ and $\alpha$ parameters, which we fit with the same
methods. As shown in the top panel of Figure 3, we are able to recover the
true $\tau_{\rm break}$ to better than $50\%$ uncertainty independent of the
input $\alpha$ value. The middle panels of Figure 3 shows examples of mean-
normalized Sphinx20 SFHs sorted by $\tau_{\rm break}$ obtained from our fits.

The resulting combination of $\sigma_{\rm MS}$ and $\tau_{\rm break}$ suggests
that we would be likely to identify a galaxy with similar properties as JADES-
GS-z7-01-QU in the Sphinx20 volume. Indeed, we can calculate the probability
in more detail, by simulating synthetic spectra from the SFHs of the Sphinx20
galaxies directly, following the same recipe as outlined in Sections 2.2.1 and
2.2.2. Doing so suggests that about $60-80\%$ of the simulated galaxies go
through similar observed properties as JADES-GS-z7-01-QU during their lifetime
until $z=7$ (lower left panel, Figure 3). The average “quiescence time“ of
galaxies with such observed properties is less than $20\,{\rm Myrs}$ on
average (lower right panel).

##  3 Impact of Dust Attenuation on Observed Quiescence

In the previous section, we have studied how a stochastic SFH can lead to the
observed properties of a galaxy like JADES-GS-z7-01-QU. Abrupt changes in the
SFH are likely the dominant reason (see also Gelli et al., 2023), however, a
blue$+$red composite spectrum could reconcile these observations as well, as
reasoned below.

Several studies based on ALMA observations suggest a significant fraction of
dust in galaxies even at $z\sim 7$ (e.g., Faisst et al., 2020a; Fudamoto et
al., 2020, 2021; Inami et al., 2022; Mitsuhashi et al., 2023). Furthermore,
thanks to large ALMA surveys such as ALPINE (Le Fèvre et al., 2020; Béthermin
et al., 2020; Faisst et al., 2020b) and REBELS (Bouwens et al., 2022), we have
statistical evidence of spatial displacement between UV and far-IR emission in
high-$z$ galaxies (e.g., Fudamoto et al., 2021; Jones et al., 2020; Béthermin
et al., 2023; Devereaux et al., 2023). This suggests distinct regions of high
and low dust obscuration. Similar conditions have been observed in quiescent
galaxies at later times (e.g., Morishita et al., 2022; Lee et al., 2024).

In fact, there is observational evidence of a spatially variable dust
attenuation in JADES-GS-z7-01-QU. The left panel of Figure 4 shows a color
composite based on the JWST/NIRCam filters F115W, F210M, and F410M. To
generate this image, we fit the two components (visible in the blue filters,
see insets) using Galfit (Peng et al., 2010a) with positional priors based on
F115W. The model images are then convolved by the F115W PSF and combined to an
RGB image. The color-composite shows a blue component (east), separated from
the main redder component (west). The color gradient could indicate spatially
variable dust obscuration inside the galaxy or a blue merger component.

Given these observations, the spectrum of JADES-GS-z7-01-QU could also be
reconciled by a blue$+$red composite as shown on the right panel of Figure 4.
We model the blue component as a young ($40\,{\rm Myr}$), constant star-
forming dust-free UV-emitting region that exhibits a high continuum escape
fraction due to a density bounded nature. In such a setting, the ionizing
radiation is escaping without producing significant nebular line emission
(e.g., Rodríguez et al., 2009; Raiter et al., 2010; Topping et al., 2022). The
search for such blue systems is ongoing with JWST and a few candidates have
been found at $z>7$ (see Topping et al. 2022 and references therein). The blue
component would have a steep UV continuum with weak nebular emission. In
contrast, the red component is modeled as a post-starburst $60\,{\rm Myr}$ old
dust-obscured region (maybe a post-starburst). Attenuation from dust generally
impacts nebular emission more than the stellar continuum. This differential
dust attenuation factor333$f~{}=~{}{\rm E(B-V)_{stellar}}/{\rm
E(B-V)_{nebular}}$. can reach $\frac{1}{f}=\frac{1}{0.44}\sim 2.3$ (e.g.,
Calzetti et al., 2000; Kashino et al., 2013; Reddy et al., 2015). Although $f$
is closer to unity at higher redshifts, a clumpy dust distribution can
significantly alter this value and in the following we assume $f=0.44$ as
worst case. Such a significantly dust obscured region could be devoid of
emission lines (low $f$) and contributing little to the UV continuum.

The right panel of Figure 4 shows a possible resulting spectra. Without dust
attenuation, the red component contributes significantly to the emission lines
of the composite spectrum (upper panel). If E(B-V) = 0.6 mag (lower panel),
the composite spectrum is practically devoid of emission lines. However, note
that due to the redder wavelength of H$\alpha$, this line should be detected
(but in the case of JADES-GS-z7-01-QU it is outside of the JWST/NIRSpec
wavelength range). This situation resembles the spectrum of the quiescent
galaxy found at $z=5.6$, which is devoid of strong optical emission lines
except AGN-broadened H$\alpha$ (Carnall et al., 2023a).

Figure 5: Maximum variation in attenuation factor of [O III] among $10$
different sight lines as a function of median color excess of the stellar
continuum in the Sphinx20 simulation. The calculation assumes a Calzetti et
al. (2000) starburst reddening curve and a differential dust attenuation
factor of $f=0.44$ (open symbols; Calzetti et al., 2000) and $f=0.7$ (closed
symbols; Kashino et al., 2013). The extinction factor can change up to a
factor of $10$ in certain cases (for $f=0.44$) due to geometric viewing
angles, resulting in an important effect on the observation of nebular
emission lines such as [O III].

The Sphinx20 simulation release provides a set of measurements along $10$
different sight lines for each of the simulated galaxies. We use the stellar
continuum color excess E(B-V) measured along the different sight lines as an
estimate of the patchiness of dust in these simulated $z=7$ galaxies.
Specifically, we can study the impact dust attenuation on nebular emission
lines by computing the attenuation factor (i.e. $10^{\frac{0.4}{f}\,{\rm
E(B-V)}\,k^{\prime}(\lambda)}$) of [O III] along these sight lines. For
$k^{\prime}(\lambda)$ we assume a starburst reddening curve (Calzetti et al.,
2000). Figure 5 shows the variations of the [O III] attenuation factor as a
function of median E(B-V) (measured over all 10 sight lines) for two
differential dust attenuation values of $f=0.44$ (Calzetti et al., 2000) and
$0.7$ (Kashino et al., 2013). This test shows that the attenuation of nebular
emission lines can change by factors of a few depending on the line of sight.
In the most extreme case we find that the attenuation changes by a factor of
$10$, which could have a significant impact on the detection of the line (i.e.
an emission line at $10\sigma$ above the flux limit would be observed at only
$1\sigma$).

The simulation therefore verifies the possibility of almost dust-free regions
along with heavily dust-obscured patches. Overall, this shows that the
spectrum of JADES-GS-z7-01-QU can be naturally explained by a composition of a
blue and red spatial component.

##  4 Summary & Conclusions

The seemingly quiescent galaxy JADES-GS-z7-01-QU at $z=7.3$ is a curious case.
The combination of lacking emission lines and blue UV to optical ratio is
difficult to reconcile in simulations and theoretical models. Specifically, a
galaxy like JADES-GS-z7-01-QU would not be classified as “quiescent” on common
color diagrams (Figure 1), but rather as a galaxy with a short-term deficit of
star formation. We have explored the observational properties of galaxies such
as JADES-GS-z7-01-QU in relation to (i) a stochastic varying (bursty) star
formation, and (ii) a spatially varying dust attenuation causing a blue and
red composite spectrum.

The observed properties of JADES-GS-z7-01-QU can be achieved shortly after a
starburst (Figure 1). From exploring more realistic stochastic SFHs, we
learned that a short star formation correlation time ($\tau_{\rm
break}<200\,{\rm Myr}$) is necessary to explain the galaxy’s observed
properties under the reasonable assumption of an average star formation
oscillation amplitude around the mean of $\sigma_{\rm MS}=0.5-0.6\,{\rm dex}$.
The average time spend in such a configuration is on the order of $10\,{\rm
Myrs}$ or less (Figure 2). A comparison to more realistic SFHs from the
Sphinx20 cosmological hydrodynamic simulation for similar-mass $z=7$ galaxies
showed that about $60-80\%$ of simulated galaxies went through this state of
temporary quiescence in the past (Figure 3).

In addition to a stochastic SFH, we explored the effect of spatially varying
dust attenuation within a galaxy (or as part of a merging system) on the
observed UV continuum and emission lines. We showed that a composite spectrum
based on the combination of a UV-bright dust-free density bounded region and a
red dust-obscured post-starburst region can result in a spectrum similar to
the one of JADES-GS-z7-01-QU (Figure 4). Specifically, the density bounded
region results in a steep UV continuum, while a high differential dust
attenuation between stellar continuum and nebular emission of the red
component leads to the suppression of nebular emission. Due to the lesser dust
attenuation at longer wavelengths, we would still expect the galaxy to show
H$\alpha$ emission in this case (unfortunately not covered by current NIRSpec
observations). Based on the Sphinx20 simulation set, we found that variations
in the attenuation of the nebular lines due to dust along different sight
lines can vary from factors of a few up to a factor of $10$ (in one case,
Figure 5). This is in agreement with UV and infrared emission offsets in
recent ALMA observations. NIRCam imaging of JADES-GS-z7-01-QU does reveal a
blue and red component, which could therefore naturally explain the observed
spectrum.

In summary, both stochastic SFH and dust obscuration can explain the spectrum
of JADES-GS-z7-01-QU. Both cases are connected and are therefore not
exclusive. Upcoming observations with ALMA of the far-infrared continuum and
the C II line emission (#2023.1.00521.S, PI: Faisst) will shed more light on
the dust-obscured star formation of JADES-GS-z7-01-QU, which will help to
constrain the above theories.

## References

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