FlashWorld: High-quality 3D Scene Generation within Seconds
Authors: Xinyang Li
Tengfei Wang
Zixiao Gu
Shengchuan Zhang
Chunchao Guo
Liujuan Cao
Venue: ICLR 2026 Oral (2026)
Keywords: 3D Scene Generation
Multi-view Diffusion Models
World Models
Distribution Matching Distillation
Primary area: generative models
TL;DR: a generative model that produces high-quality 3D scenes from a single image or text prompt in seconds

Abstract: We propose FlashWorld, a generative model that produces 3D scenes from a single image or text prompt in seconds, $10 \sim 100\times$ faster than previous works while possessing superior rendering quality.
Our approach shifts from the conventional multi-view-oriented (MV-oriented) paradigm, which generates multi-view images for subsequent 3D reconstruction, to a 3D-oriented approach where the model directly produces 3D Gaussian representations during multi-view generation.
While ensuring 3D consistency, 3D-oriented method typically suffers poor visual quality.
FlashWorld includes a dual-mode pre-training phase followed by a cross-mode post-training phase, effectively integrating the strengths of both paradigms.
Specifically, leveraging the prior from a video diffusion model, we first pre-train a dual-mode multi-view diffusion model, which jointly supports MV-oriented and 3D-oriented generation mode. 
To bridge the quality gap in 3D-oriented generation, we further propose a cross-mode post-training distillation by matching distribution from consistent 3D-oriented mode to high-quality MV-oriented mode. 
This not only enhances visual quality while maintaining 3D consistency, but also reduces the required denoising steps for inference.
Also, we propose a strategy to leverage massive single-view images and text prompts during this process to enhance the model's generalization to out-of-distribution inputs.
Extensive experiments demonstrate the superiority and efficiency of our method.
Our code is released at https://github.com/imlixinyang/FlashWorld.

Published as a conference paper at ICLR 2026
FLASHWORLD: HIGH-QUALITY 3D SCENE GENERA- TION WITHIN SECONDS
Xinyang Li Tengfei Wang?, Zixiao Gu², Shengchuan Zhang', Chunchao Guo?, Liujuan Cao' 1 Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 2 Tencent, Yes Lab, Fudan University
ABSTRACT
We propose F FlashWorld, d, a generative model that produces 3D scenes from a single image or text prompt in seconds, 10 ~ 100x faster than previous works while possessing superior rendering quality. Our approach shifts from the conven- tional multi-view- oriented (MV-oriented) paradigm, which generates multi-view images for subsequent 3D reconstruction, to 3D-oriented approach where the model directly produces 3D Gaussian representations during multi-view genera- tion. While ensuring 3D consistency, 3D-oriented method typically suffers poor visual quality. FlashWos ld includes a dual-mode pre-training phase followed by a cross-mode post-training phase, effectively integrating the strengths of both paradigms. Specifically, leveraging the prior from video diffusion model, we first pre-train a dual-mode multi-view diffusion model, which jointly supports MV-oriented and 3D-oriented generation modes. To bridge the quality gap in 3D-oriented generation, we further propose a cross-mode post-training distilla- tion by matching distribution from consistent 3D-orieeted mode to high-quality MV-oriented mode. This not only enhances visual quality while maintaining 3D consistency, but also reduces the required denoising steps for inference. Also, we propose strategy to leverage massive angle-view images and text prompts during this process to enhance the model's s generalization to -iitt--disttiuution inputs. Ex- tensive experiments demonstrate the superiority and efficiency of our method. Our code is released at https: : tht.. com/ im1 x nyang// /F aashooordd.
Image Prompt
Chickenuu
in forest.
Seconds
1
High-quality SDGS Scene
Figure I: FlashWorld d enables fast and high-quality 3D scene generation across diverse scenes.
INTRODUCTION
3D generation shows great promise for applications in gaming, robotics, and VR/AR. However, generating full 3D scenes remains significant challenge for both quality and efficiency, compared to generating individual 3D objects. These challenges stem from two core obstacles: the scarcity of high-quality 3D scene data and the exponential complexity of modeling real-world scenes.
Work done during an internship at Tencent.
1 'Correponning Authors.

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Input
CAT3D
Bolt3D
Wonderland
77 min (A100 GPU)
15 (A100 GPU)
5 min (110 GPU)
Ours w/ MV-Diff
Ours w/ 3D-Diff
Ours w/ MV-Dist
Ours
min (H20 GPU)
1 min (H20 GPU)
7 sec (H20 GPU)
9 sec (H20 GPU)
Figure 2: brief comparison different 3D scene generation methods. MV-oriented diffusion methods (i.e., CAT3D (Gao et al., 2024), Bolt3D (Szymanowicz et al., 2025), Wonderland (Liang et al., 2025), and Ours w/ MV-Diff) suffer from noisy textures due to multi-view inconsistency. MV-oriented distillation further exacerbates this flaw (i.e., Ours w/ MV-Dist). 3D-oriented diffusion methods (i.e., Ours w/ 3D-Diff) suffer from blurry visual effect. Our cross-mode distillation model (i.e., Ours) simultaneously solves these, making the quality of the novel view close to the input view. The time cost per scene, tested on single GPU, is presented at the bottom of each method.
Early methods typically relied on assembling pre-existing 3D assets (Xu et al., 2002; Yu et al., 201 1; et 2018; Feng al., 2023; Çelen et al., 2024; Yang al., 2024c; Deng al., 2025) or iteratively reconstructing scenes from inpainted images and depth maps (Cai et al., 2023; Fridman et al., 2023; Höllein et al., 2023; Lei al., 2023; Yu et al., 2024a; Zhang al., 2024a;b; Chung et al., 2023; Yu et al., 2025; Shriram et al., 2025; Ni et al., 2025). Yet, without holistic scene-level understanding or multi-view consistency constraints, these approaches often struggle to produce semantically coherent and visually realistic scenes. To address this, scalable data-driven approaches have emerged. The dominant paradigm is two-stage, multi-viee-ooriented (MV-oriented) pipeline (Gao et al., 2024; Sun al., 2024; Wallingford et al., 2024; Zhao et al., 2025; Szymanowicz al., 2025; Yang et al., 2025; Go et al., 2025a;b): diffusion model first generates multiple views from text or reference images, and then 3DD reconstruction is performed. However, the lack of explicit 3D constraints during view synthesis often causes geometric and semantic inconsistencies in generated views, leading to noticeable visual quality gap between synthesized views and the reconstructed 3D scene. Moreover, the considerable computational overhead of both the diffusion and reconstruction stages leads to generation latencies of several minutes to hours, as shown Fig. 2. These limitations compromise the effectiveness and efficiency of current 3D scene generation methods, blocking their applications.
One promising but relatively less explored direction is the 3D-oriented scene generation pipeline (Xu et al., 2023; Li et al., 2024a;b; Tang et al., 2025; Cai et al., 2024; Zuo et al., 2024). These methods combine differentiable rendering (Mildenhall et al., 2020; Wang et al., 2021; Kerbl et al., 2023) with diffusion models. allowing for direct 3D scene generation without additional reconstruction. However, these generated 3D scenes often suffer from visual artifacts and blurry content. Consequently, they often require additional refinement stage, which significantly degrades generation efficiency.
To enhance efficiency of diffusion models, post-training distillation techniques, such as consistency model distillation (Song al., 2023) and distribution matching distillation (Yin et al., 2024b;a; Xie et al., 2024), are often used. However, directly applying distillation amplifies each framework's inherent limitations: e.g., exacerbates multi-view inconsistency in the MV-oriented pipeline.
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In this work, we introduce novel framework that combines the strengths of both paradigms through distillation, achieving substantial gains in 3D consistency and visual fidelity while significantly accelerating inference speed. Our contributions are briefly summarized as follows:
We introduce a dual mode pre-training strategy built on a video diffusion model to train a multi-view diffusion model capable of operating in both MV-oriented and 3D-oriented modes.
We propose a cross-modd post-training strategy, where the MV-oriented mode serves as the teacher to improve visual quality, while the 3D-oriented mode acts as the student to ensure 3D consistency.
To improve disttiibbittnii generalization ability, we introduce a novel strategy that can leverage massive unlabeled image data and text prompts with randomly simulated camera trajectories during post-training, enhancing the model's adaptability to diverse inputs, as shown in Fig. 1.
2 PRELIMINARY
Diffusion models (Ho et al., 2020) generate data by progressively transforming samples from a standard Gaussian distribution p(ær) (O,0,) I) into samples from a target data distribution p(z), which have been widely applied across multiple domains, including image synthesis (Rombach et al., 2022), multi-view generation (Shi et al., 2023; Tang et al., 2023), video generation (Blattmann et al., 2023; Wan et al., 2025), and panoramic 3D scenes (Hunyuan World, 2025). The core methodology involves training a denoising network with optimizable parameters to reconstruct the original data by removing the injected Gaussian noise from according to predefined noise schedule. The forward process formulated as: x4 = Fxx,,) t) = a + ate, where a and or jointly control the signal-to-noise ratio at each timestep t. The denoising network can be trained to predict clean data A from noisy input ze by minimizing the following objective:
C - Ex,r, [| (0 0)]
Alternative training objectives include predicting noise c (Ho et al., 2020) or a linear combination of zo and c, known as u-prediction (Salimans & Ho, 2022). All predictions can be converted the denoised estimate u(=e, 1) and represent the gradient of the log probability of the distribution:
s(x,, t) Vz, log Peg:)(())) = It a) t)
(2)
of
Distribution matching distillation (DMD) (Yin et al., 2024b;a) is advanced technique designed to distill a slow, multi-step teacher diffusion model into a fast, few-step student model with comparable generation capabilities. The key component to minimize the approximate KL divergence across randomly sampled timesteps t and noise inputs a between the smoothed real data distribution (re) and the student generator's output distribution Prake (x+) by:
CDDD =-E (ʃ (F
Stake (F((G dGo(2) dz d0
where Seeal and Stake are approximated scores using diffusion models freat and Plke trained on their respective distributions (Eq. 1). DMD uses frozen pre-trained diffusion model freal as the teacher, and dynamically updates Plake while training Gg, using diffusion loss on samples from the generator.
3 METHOD
The core of our framework lies in leveraging DMD to transfer knowledge from a MV-oriented multi-view diffusion model, one well-established for high visual quality, 3D-oriented few-step multi-view generator, which is inherently endowed with 3D consistency. However, this paradigm introduces two key challenges: First, for open-world 3D scene generation, the 3D-oriented few-step generator requires sufficiently robust prior and strong generative capacity from the start. Without this, the training process is prone to collapse. Second, due to the limited quantity and diversity of high-quality multi-view datasets, it becomes critical to develop strategy that effectively handles scenarios with diverse styles, object categories, and camera trajectories. Specifically, To address these challenges, we first design dual-mode pre-training strategy as detailed in Sec. 3. This strategy
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mual- Pre training
Cross mode Post training
Input Views
Conditions
Noise
Add Naise
Add
Novel Views
DIT Blocks
DiT Blocks
Nuise
JDGS
3DGS
JDGS
Novel
Input Cameras
3DGS
Novel
DIT Blocks
DiT Blocks
Cameras
Render
Render
Real Score
Fake Score
Cap
Lome
LOMD
Figure 3: Method overview. We first pre-train dual-mod multi-view latent diffusion model using multi-view datasets, and then employ an cross-mode distillation post-training strategy to accelerate generation while enhancing visual quality and inheriting 3D consistency.
yields multi-view diffusion model that operates in two distinct modes: a MV-oriented mode for high visual fidelity and 3D-oriented mode for inherent 3D consistency. Subsequently, in Sec. 3.2, we present a cross-mode post-training framework bridge these two modes: the MV-oriented mode acts as the teacher, supplying score distillation gradients to ensure visual quality; the 3D-oriented mode serves as the student, learning to inherit the teacher's distribution while preserving 3D consistency. Furthermore, to explicitly tackle -ittr--diitribttion generalization, in Sec. 3.3, introduce strategy that can leverage single-view image data, text prompts, and pre-defined camera trajectories, boosting the model's adaptability diverse scenarios.
3.1 DUAL-MODE PRE-TRAINING
In this stage, we trait a dual-mode multi-view latent diffusion model using multi-view datasets, as illustrated in Fig. (left). For each training iteration, we sample a batch containing multi-view images X, their corresponding camera parameters C, and additional conditioning information y (such as a text prompt or a single-view image). The multi-view images are first encoded into the latent space to obtain multi-view latents 2 FE()). A forward diffusion process is then applied to produce noisy multi-view latents Z, = 2 + O&E at a randomly sampled timestep
The noisy latents Z, together with the camera parameters C' and conditioning y. input to the denoising network for reverse denoising training. We represent cameras using Reference-Pore Point Plücker Coordinates (Cai et al., 2024) raymaps. The denoising network is Diffusion Transformer (DiT) (Peebles & Xie, 2023) enhanced with 3D attention blocks, and outputs both denoised estimate 2xy and an auxiliary multi-view feature F. For the MV-oriented mode, we optimize:
|| 3 ZMV
Luv Ex..,.
To enable 3D-oriented generation, we decode 3D Gaussian parameters from the multi-view feature F using 3DGS decoder: {T, q, 8, a, c) (F),), where q, s, and represent the depth, rotation quaternion, scale, opacity, and spherical harmonics coefficients the 3D Gaussians, respectively. The 3DGS decoder DG is initialized from the original latent decoder D, with its first and last convolutional layers re-initialized to accommodate the additional features and output channels required for the Gaussian parameters. The predicted depth then converted to pixel-aligned Gaussian points via o + rd, where and d denote the camera origin and ray direction, respectively. For the 31D-oriented mode, we optimize the following loss:
Lap Ex...yy [|aaevel R(G, Coovee))
(5)
where denotes the rendering operation, g = {u, q. s, a, c} is the set of 3D Gaussians, and Xnovell Crevel are the ground-truth novel-view images and their associated cameras, respectively. During
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inference, both MV-oriented and 3D-oriented modes be used for denoising (Li et al., 2024a;b). In particular, for the 3D-oriented mode, the model predicts the estimated clean multi-view latents as 2nD = E C))
In contrast previous methods (Li et al., 2024a;b) that are initialized from image diffusion mod- els (Rombach et al., 2022), we initialize our framework with a video diffusion model (Wan et al., 2025). We observe that this video model not only converges more rapidly, but also features powerful VAE with a higher compression rate, enabling support for larger number of views (i.e. 24) and higher output resolutions (i.e., 480P).
3.2 CROSS-MODE POST-TRAINING
After pre-training, we employ an asymmetric distillation strategy accelerate generation while enhancing visual quality and inheriting 3D consistency, as shown in Fig 3 (right). Specifically, we observe that while the MV-oriented mode exhibits poor consistency, it can generate multi-view images with high visual quality; thus, we leverage the MV-oriented mode of our dual-mode multi-view latent diffusion model as the real teacher Preal: this teacher model frozen, tasked with computing the real score gradient. Another copy of the model jfaee is dynamically updated to estimate the fake score corresponding to the current distribution the distilled generator. Meanwhile, our few-step student model is initialized with the 3D-oriented mode of our dual-mode multi-view latent diffusion model.
The 3D-oriented multi-view generation process alternates between denoising and noise injection steps to enhance sample quality of the 3D scenes following LCMs (Luo et al., 2023). Specifically, we first define a schedule of N timesteps, denoted as {f1, f2, ... ,ty J. where is typically small IO, we alternate between 3D-
(e.g., 4). Starting from randomly sampled noise Z,, (G, t. N...Y.C.... C)) and forward diffusion steps Zt._. 0r,11 2. + Itul1 where Gø.DD is the 3DGS generator and ~ (N, I), until obtaining the 3D Gaussians the final step (i.e., Ge.ND x IN y/, C)). At each step. the multi-view denoising update is performed based on rendering, thereby ensuring that 3D consistency is maintained throughout the process.
oriented denoising updates 2.
During distillation training, we adopt the DMD2 algorithm (Yin et al., 2024a), which includes DMD objective (i.e., Eq. 3) and standard non-saturating GAN objective (Goodfellow et al., 2020), where the logits value required by the GAN loss is obtained by adding an extra classification branch with several convolutional layers at the end the fake score network. We adopt the estimated R1 regularization (Lin et al., 2025a) to stabilize the GAN training. The DMD objective and the GAN objective are employed to optimize both the original and novel views.
We also observe that relying solely the above strategy can lead to the generation of scenes with unstable floating artifacts. We hypothesize that this instability arises from the challenges in optimizing with noisy gradients introduced by Gaussian rendering and latent encoding. To address this, during post-training, we additionally update an MV-oriented student model at a lower frequency. This model shares the same DiT backbone as the 3D-oriented student model. To encourage alignment between the two modes, we introduce a cross-mode consistency loss:
Lcmc = E...e.y.C.s
||IE(R(Go, (..,C),, (o,, v..
(6)
where À is small weighting factor (i.e., 0.1). Because the MV-oriented mode prediction are less affected by unstable rendering gradients, this consistency loss regularizes the 3D-oriented mode to produce more stable and reliable generations.
3.3
DATA CO-TRAINING.
During pre-training, it is common to jointly train on image and video generation tasks to enhance the model's generalization ability. While this approach benefits the DiT backbone, it does not optimize the 3DGS decoder, potentially limiting the range of inputs the 3DGS decoder can effectively process. To address this, in the post-training phase, we introduce a strategy to broaden the model's 's input distribution and improve generalization to diverse scenes, quantity and variety. Specifically, we combine image or text conditions sampled from image datasets with random camera trajectories, which can be drawn either from multi-view sequences from
when multi-view data is limited in
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Ours
CAT3D
Bolt3D
Wonderland
Baselines
Figure 4: Image-to-3D scene generation results of different methods.
set of predefined trajectories. Importantly, omit the GAN loss during this co-training process to prevent distribution mismatches. This approach not only enhances the model's generalization to wide range of input images and text prompts, but also increases its robustness when encountering out-of- out-of-distrine distribbutioon camera trajectories. The details this strategy are provided in Appendix A.
4 EXPERIMENTS
In this section, we evaluate the performance of our method on various benchmarks, including image- to-3D scene generation, text-to-3D scene generation, and WorldScore benchmark. For implementation details, please refer Appendix
4.1 COMPARISON ON IMAGE-TO-3U SCENE GENERATION
We present a qualitative comparison with state-of-the-art image-to-3D scene generation methods in Fig. 4. These baselines MV-oriented, including: CAT3D (Gao et al., 2024). which generates novel views via multi-view diffusion followed by optimization- based 3D reconstruction; Bolt3D (Szy- manowicz al., 2025), which synthesizes both appearance and geometry for novel views and then applies a feed-forward 3D reconstruction; and Wonderland (Liang et al., 2025), a leading approach that leverages a powerful video diffusion model and latent-based feed-forward 3D reconstruction. As these methods are not open-sourced, we utilize the video results provided in their respective project pages for visualization. We employ ViPE (Huang et al., 2025) to estimate camera poses and intrinsics from the baseline videos. CAT3D struggles to generate complex scenes, resulting in blurry outputs and missing geometric details. Bolt3D also exhibits inaccurate geometric details, such as imprecise tree branches and needle-like leaves. Wonderland suffers from repeated and distorted Gaussian artifacts, especially under large camera pose changes. Overall, these MV-oriented methods fail to generate complex scenes, primarily due to insufficient multi-view consistency. In contrast, our model produces high-fidelity, detailed scenes and successfully recovers intricate structures (e.g., leaves, iron fences, and tentacles), highlighting the advantages of our 3D-oriented pipeline.
4.2 COMPARISON ON TEXT-TO-31 SCENE GENERATION
We compare our method against several state-of-the-arr text-to-3D scene generation approaches, including Director3D (Li et al., 2024b), Prometheus (Yang et al., 2025). SplatFlow (Go et al., 2025a), and VideoRFSplat (Go al., 2025a). qualitative comparison is presented in Fig. 5. Director3D relies on per-scene refinement, which frequently introduces blurry and wave-like artifacts in the generated results. In contrast, our model produces accurate objects with fine- imn-igrined details, such animal fur, while preserving realistic backgrounds. Prometheus does not utilize refinement, and due to the inherent inconsistency of its MV-oriented pipeline, the generated scenes are often blurry and may exhibit incorrect object geometries (e.g., chair legs). Our approach, however, is

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Inpur
A spacious kitchen with wooden floors, white countertops, and an island center.
A traditional wooden gate with red lanterns.
A large stone fountain surrounded by lush greenery and clear sky.
fluffy, orange cat.
Ours
Director3D
Prometheus
SplatFlow
VideoRISSpla
Baselines
Figure 5: Text-to-3D scene generation results different methods.
Method
Q-Align IQA
Q-Align IAA
T3Bench-200
CLIP
CLIP
CLIP Samm
Q.Aligu 1QA
Q-Align 1AA
DL3DY-200
CLIP
CLIP Arehviiic
CLIP Score
Q-Align 1QA
Q.Alisi IAA
WoeldS 200
CLIP
CLIP
CLIP
Tim Cos
10
Acchetic
IQA+
K0A+
Arailatic
Seree
DimesooDD
3:24
1.95
0141
1.
27.84
2.51
1.78
0.34
4.59
26.12
1.55
2447
0.35
5:32
20.00
min
Promihers
2:34
1.93
2007
199
4.89
8.6
386
4.77
5.0
Table 1: Quantitative comparison on text-to-3D scene generation. Cell background colors indicate the method is the best second best
third best this metric.
capable of generating structurally rich and precise objects in complex scenes, even under large camera movements. SplatFlow and VideoRFSplat also suffer from blurry artifacts and have difficulty reproducing fine details, such as those found in floors and grass. In comparison, realistic details while maintaining semantic consistency with the input text prompt.
model generates
We further perform comprehensive quantitative evaluation for this task. Specifically, sample 600 text prompts from T3Bench (He et al., 2023), DL3DV (Ling et al., 2024), and WorldScore (Duan al., 2025), covering object-centric and general scenes. As all compared methods based on 3D Gaussian representations, metrics related to camera control and 3D consistency are not applicable in this setting. Accordingly. we concentrate on the quality evaluation metrics utilized, including CLIP IQA+ (Wang et al., 2023), CLIP Aesthetic (Schuhmann, 2022), the text-image alignment score (CLIP Score) (Hessel al., 2021), 1), as well as the latest LMM-based Q-Align (Wu et al., 2024) image quality metric. The quantitative results are summarized in Tab. 1. is evident that our model achieves superior performance on the majority of quality evaluation metrics. For Aestheec we note that this metric sometimes favor smooth outputs, which may not always align with the detailed and realistic results produced by our method. Our method also attains the highest CLIP Score for two subsets, demonstrating the strong text alignment ability of our method. In addition, we report the average time required to generate a single scene for each method on a single H20 GPU. Our method demonstrates substantial speed advantage over other approaches. Remarkably, this efficiency is
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Ours
WonderWord
LucidDreamer
WonderJourney
Figure 6: 3D scene generation results of different methods on WorldScore benchmark.
3D
Photometrie Consistency
Object Control
Content Alignment
Style Consistency
Subjective Quality
Time
Method
Average
Consistency
Cost
WonderJoumey
80.60
79.03
34.81
38.37
67.52
61.49
60.30
LucidDreamer
90.37
90.20
43.48
59.41
66.41
48.02
66.32
min
WonderWorld
86.91
85.56
52.09
56.82
75.92
41.28
66.43
10 sec
Ours
85.87
86.72
49.61
53.96
81.52
54.63
68.72
9 sec
Table 2: Quantitative comparison on WorldScore benchmark. Note that the time cost of the baselines is tested on H100 GPU, while our time cost is tested on H20 GPU.
maintained even when our method produces results with higher resolution and greater number of frames. In addition, our approach leverages unified model that seamlessly handles both image-to-3D and text-to-3D tasks without requiring separate training processes. This unified framework not only simplifies the overall workflow but also substantially reduces the training cost.
4.3 COMPARISON ON WORLDSCORE BENCHMARK
We further conduct comprehensive evaluation the recent WorldScore (Duan et al., 2025) bench- mark. The static subset of WorldScore comprises 2,000 test examples, encompassing a diverse array of worlds with varying styles, scenarios, and objects. Each test case provides an input image, a text prompt, and a camera trajectory as conditions for generation. The evaluation protocol is designed to assess primary aspects of world generation: controllability and quality. For baselines, select three state-of-the-an 3D generation methods: WonderJourney (Yu et al., 2024a), which itera- tively completes novel-view images and depth maps based on point clouds; LucidDreamer (Chung et al., 2023), which also performs iterative novel view completion but utilizes 3DGS for rendering: and HonderWorld (Yu et al., 2025), which improves generation quality through the use of layered Gaussian surfels. Since our comparison focuses exclusively on 3D generation methods, the "Camera Control" metric primarily reflects the robustness of the evaluation protocol for each method, and is thus less informative in this context. Accordingly, we omit this metric from our comparison. Additionally, the original WorldScore benchmark evaluates most metrics only on anchor frames, which is suboptimal for 3D world generation tasks that require novel view synthesis. To ensure fairer comparison, we re-evaluate these metrics by randomly sampled frames within specific intervals. shown in Fig. 6 and Tab. 2, respectively. Our method
Qualitative and quantitative comparisons achieves the highest average score and the fastest inference speed among all compared approaches. In partieular, model achieves the best "Style Consistency" and secures the second place in "Pho-
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13Beach-300
WorldScore-300
Contig.
Q-Align
Q-Aliga TAA
Q-Align
Q-Align
CLIP
CLIP Aesthetic
CLIP
Q-Aliga
Q-Alige
CLIP
IQA
IQA+
Amaheric
Score
IQA
1AA
IQA+
Score
IQA
IAA
IQA+
Aesthetic
Score
3.11
2,00
0.41
4.36
25.34
2.60
2.09
0.39
4.50
24.19
2.45
2.10
4.78
27.80
2,61
1.68
0.37
4.11
22.92
2.71
0.40
4.54
22:71
2:74
2.16
0.33
4.83
26.11
3.46
2.12
0.45
4.42
26.95
2.99
2.05
0.42
4.57
26.41
3.06
2.18
0.42
4.92
28.71
4.12
2.31
0.92
4.53
27.50
4.02
2.35
0.51
4.90
27.90
3
2:71
0.51
5.12
20.09
E
3.88
2.50
0.53
4.58
27.04
3.84
2.35
0.50
4,83
27.45
3.00
2.56
0.47
4.55
28.76
4.12
2.36
0.54
4.89
27.08
3.9
2.27
0.50
4.77
27.63
3.76
2.55
0.49
5.08
29.13
Table Quantitative ablation studies. The letters A-F correspond to different model variants: (A) w/ MV-Diff, (B) w/ 3D-Diff, (C) w/ MV-Dist, (D) w/o CMC, (E) w/o OOD, and (F) Full model.
w/ MV-Diff
w/ 3D-Diff
w/ MV-Dist
w/o CMC
w/o OOD
Full model
Figure 7: Qualitative ablation studies. Prompts: (Top) "A vintage clock hanging on brick wall"; (Bottom) "A bright sunflower field".
tometric Consistency", "Object Control", and "Subjective Quality", reflecting a well-balanced and robust capability across controllability and quality. While method yields relatively lower scores in "3D Consistency" and "Content Alignment", these results can be attributed to methodological differences: for "3D Consistency". all baselines utilize monocular depth estimation models that closely aligned with the evaluation protocol, whereas our approach relies solely on RGB supervision without explicit depth guidance: for "Content Alignment", our method does not directly manipulate the anchor frame content, in contrast to the baselines. Qualitative analysis further reveals that baseline methods frequently exhibit unnatural transitions, discontinuous content, and visible holes in the generated scenes, which may not be fully reflected by the current metrics. Overall, our approach demonstrates superior consistency and faithful generation over existing methods.
4.4 ABLATION STUDY
In Fig. 2, we show the generation results of various ablation models for image-to-3D -o scene generation. The outcomes align well with our expectations: both the MV-oriented diffusion model (w/ MV-Diff) and the MV-oriented distillation model (w/ MV-Dist) exhibit noisy 3D reconstruction due to multi- view inconsistency, while the 3D-oriented diffusion model (w/ 3D-Diff) produces blurry visual results. To further validate the effectiveness of each proposed strategy, we conduct more comprehensive ablation studies on text-to-3D scene generation. Quantitative and qualitative results are summarized in Tab. and Fig. 7, respectively. Consistently, the first three ablation models continue to demonstrate worse visual quality and weaker text alignment. The model without cross-mode consistency loss (w/o CMC) achieves competitive, and in some cases superior, scores most quantitative metrics compared to our full model. However, qualitative analysis reveals that this model is susceptible to floating and duplicated artifacts. The model without out-of-distribution data (w/o OOD) is more prone to semantic misalignment (e.g.. "field") and exhibits a drop in quantitative text alignment metrics. This issue is exacerbated on T3Bench and WorldScore. which differ in distribution from the original multi-view data, highlighting the importance of incorporating OOD data to improve generalization.
CONCLUSION
We propose an efficient yet powerful model for 3D scene generation, named F FlashWorld. At the core of approach is novel distillation strategy, which transfers high visual fidelity from multi-vieewooriente diffusion model to a 3D-orietted multi-view generative model endowed with perfect 3D consistency. To achieve this, design dual-mode pre-training phase and cross-mode

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post-training phase, and introduce autoof-distribution data co-training strategy to boost the model's generalization. Our method achieves state-of-the of-the-art performance on multiple tasks, while offering significant advantages in inference speed. The efficiency and effectiveness of our approach well-positioned to advance applications of 3D scene generation. Future work includes incorporating autoregressive generation and extending our framework to dynamic 4D scene generation tasks.
ACKNOWLEDGMENT
This work was supported by National Science and Technology Major Project (No. 2025YFE01 13500), the National Science Fund for Distinguished Young Scholars (No. 62525605), and the National Natural Science Foundation of China (No. U25B2066, No. U22B2051, and No. 62272401).
ETHICS STATEMENT
FlashWorld d enable fast, high-quality 3D scene generation, lowering barriers for content creation in fields like gaming. VR/AR, and digital media. This democratization benefit small studios and independent creators, but also raises concerns about potential misuse (e.g., fake and harmful 3D envi- romments) and dataset bias (e.g., gender and race human-related subjects). We encourage further research on detection of AI- Al-generated 3D content and careful consideration of ethical implications in real-world applications.
REPRODUCIBILITY STATEMENT
We committed to ensuring the reproducibility of all results reported in this paper, in accordance with ICLR standards. To this end, we publicly release our codebase, pre-trained model check- points, and all scripts necessary reproduce our experiments and main results upon publication. Comprehensive details regarding model architecture, training procedures, hyperparameters, and dataset preprocessing are provided in the Appendix A facilitate independent verification. We also specify all evaluation metrics and protocols used in our experiments. Furthermore, we will provide instructions for environment setup and hardware requirements enable seamless replication.
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A TRAINING DETAILS
Architecture configuration. Our dual-mode multi-view latent diffusion model is initialized with WAN2.2-5B-IT23 (Wan et al., 2025), with the detailed architecture shown in Fig. 8. For both pre-training and post-training, we adopt 24 key frames as input views. The spatial downsampling factor from image space to latent space is set to 16. The auxiliary multi-view feature has a channel dimension of 1024. The discriminator head is a CNN with several residual blocks (He et al., 2016).
Pre-training configuration. We set the learning rates for both the transformer and 3DGS decoder 6 We use a weight decay of 1 10 and Adam (Kingma, 2014) optimizer parameters 31 = 0.9 and 32g 0.95. The training schedule includes warm-up phase of 1,000 steps, followed by a learning rate decay over 10,000 steps, with a total of 20,000 training steps. The training takes around 3 days.
to 2 10-6
Post-training configuration. During tratttrainnng, the timestep schedule for the few-step generator is set to (1000, 900, 750, 500). For each generator update, the fake score network is updated 4 times.
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The learning rates set to 1 10-6 for the generator and 5 10-? for the discriminator, both with a weight decay x 10 6 We use the Adam optimizer with 31 0.9 and B = 0.95. The training schedule consists of 1,000 step warm-up, followed by learning rate decay over 5,001 steps, and total of 10,000 training steps. The GAN loss weights for both the generator and discriminator are set to 5 x 10-3 The training takes around 2 days. The frequency of different tasks is controlled as follows: the probability ratio for training on MV-oriented mode, input views of 3D-oriented mode, and novel views of 3D-oriented mode tasks 1:3:1. The ratio for sampling multi-view data versus out-of iit-of--ditribution data is 2:1.
We use 6 precision for both training phases. The batch size is 64, using 64 NVIDIA H20 GPUs. For distributed training, we adopt the FSDP (Fully Sharded Data Parallel) strategy and activation checkpointing to improve training efficiency and memory utilization. The prediction of MV-oriented mode is actually r-prediction, following the original video diffusion model (Wan et al., 2025). We use the flow matching schedule (Lipman al., 2023) for both training phases.
Dataset configuration. For both pre-training and post-training, utilize the following multi-view datasets: (1) MVImgNet (Yu et al., 2023): an object-centric dataset with a resolution of 480x704; (2) RealEstate 10K (Zhou et al., 2018): an indoor scene dataset with a resolution of 704 488 and frame stride E 5, 6, 7, 8, 9, 10, 11, 12]: (3) DL3DVIOK (Ling et al., 2024): a general-purpose purpose scene dataset with resolution of 704 x480 and frame stride E [2,3, 4).
For out-of-distribution data during post-training, we employ: (1) Arbitrary image and text data paired with RealEstate10K and WorldScore camera trajectories: a general dataset with resolution of 480. The images and texts are sampled from proprietary video dataset. (2) Echo4O (Ye al., 2025) images with WBD (Xia al., 2024) camera trajectories: a stylized, object-centric dataset with resolution of 480
B DETAILS OF CROSS-MODE POST-TRAINING
In Sec. 3.2, we described the specific process of Cross-mode Post-Training. Here, we provide more relevant details and pseudocodes.
GAN loss. The GAN loss we introduced during our coos post-training consistent with that of DMD2 (Yin al., 2024a), which is:
LGAN = min max Be,,, [log D(F(z,t)) ( (og
(7)
D
Go
where the real data (i.e., multi-view latents X' our case), is the forwarding process of diffusion model, and / is a random timestep. The discrimator D share the same DiT network with the fake score estimator Plake- Therefore, calculating the GAN loss and fake score only requires single pass through the DiT network. When using -iitt---iitiittiinn data for distillation, there no corresponding real data. Using GAN loss in this case will lead to distribution misalignment. Therefore, ignore GAN loss when working with OOD data during post-training.
Few-step Generation Process. We adapt the multi-step generation process of DMD2 (Yin et al., 2024a) to our dual-mot moue generator to formulate the student model. Furthermore, since 3D scene generation requires the capability of novel view synthesis, our 3D generation process also needs to support novel render cameras that different from the input cameras. The specific algorithm is:
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Algorithm Few-step Generation Process
Input:
random noise (0, 1), target timestep index n, conditions y, input cameras C, render cameras C², predefined time scheduler a and o, MV-oriented generator Gg,MV, 3D-oriented generator Ga,3D, latent encoder E, and generation mode.
Output: generated multi-view latent Gg(z)
Zr, t-
2: for i =1=1,22 .. do
3: # &Reversing
4: 2r, + 5 4, 34,
5:
# #Forwarding
6:
<t-N(0, 1), 24,11 + 0t14 Zr, + 0t4t1
7: end for
8:
9: if mode = "MV" then
Go(z) 2r,, t Ga, (2,, bn
11: else
Go(z) Zr, + En C)
13: end if'
15: return Go(z)
Cross-mode Post-training Process. Our post-training procedure aligns with that of DMD2, but is tailored our specific data and generation pipeline. Specifically, as illustrated in Fig. (right), our generator can operate in two modes: MV-oriented and -D-oriented. When using 3D-orrinttdd mode, we render novel views instead of using input views for loss computation with a certain probability. Meanwhile, when novel views are not used, we compute the CMC loss (Eq. 6) to stabilize the training of the 3D-oriented generator. Notably, as the MV-oriented and 3D-oriented modes share the DiT network, CMC loss computation incurs negligible additional training overhead. For data, sample out-of- difriittiittiinn data with a certain probability during training. In such cases, as no multi-view ground truth is available, omit GAN loss computation. The detailed algorithm is:
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Algorithm 2 Cross-mode Post-training Process
Input: Regular multi-view data, OOD data, MV-oriented generator Gø.Mv, 3D-oriented generator
Gø real and fake score estimators Mreal and jfake discriminator D, latent encoder E
Output: optimized 3D-oriented generator G0,3
1: Preal - Preal
2: while not converge do
3: mode t- RandomChoice( ('"M", "3D"). weights = [1,4]
4: data <- RandomChoice( "Regular", "OOD"), weights [2, 1]) ([True,
5: isnovel +- RandomChoice (TTue, False], weights = [1,3) if mode = "3D" else False
6:
7: Sample y, C and Covel from data
8:
Sample correponding X and Xnovel from data only data "Regular"
9:
C' Crovel if isnovel else C
z (00, 1) n + RandomInt(1, N)
11:
12:
Generate Gz(z) with Algorithm
13:
14:
# Two Time-scale Update Rule in DMD2
15:
if iter % 5 0 then
16:
Optimize Ga.Mv or Gø,3D by DMD loss (Eq. 3) with Mreal and Make if not isnovel then
18:
Optimize Go.MV and Gø by CMC loss (Eq. 6) end if
20:
if data * "OOD" then
Optimize Go.mv or Gø by GAN loss (Eq. with D
22:
end if
else
24:
Optimize jrake by diffusion loss (Eq. 1)
25:
data + "OOD" then Optimize D by GAN loss (Eq. 7)
26:
27:
end if
28: end if
29:
30: iter < iter
31:
end while
32:
return G0
C RELATED WORKS
Iterative 3D scene generation. Recent advances diffusion models (Rombach et al., 2022; Podell al., 2023; Zhang et al., 2023) have enabled iterative generation of 3D scenes. DiffDreamer (Cai et al., 2023) improves multi-view consistency by conditioning on both past and future frames. SceneScape (Fridman al., 2023), Text2Room (Höllein al., 2023), and RGBD2 (Lei al., 2023) refine mesh-based representations through deptt-condittoned diffusion. WonderJourney (Yu et al., 2024a) leverages point clouds with VLM-guided re-generation. Text2NeRF (Zhang al., 2024a) and 3D-ScemeDreamer (Zhang et al., 2024b) address eттог accumulation by utilizing NeRF (Mildenhall al., 2020) representations. LucidDreamer (Chung et al., 2023), WonderWorld (Yu al., 2025), RealmDreamer (Shriram et al., 2025), and WonderTurbo (Ni et al., 2025) accelerate generation and enhance fidelity using 3DGS (Kerbl et al., 2023). While iterative generation methods have made significant progress, they often suffer from cross-vics view semantic inconsistency. In contrast, data-driven approaches leverage rich cross-view priors to better maintain semantic coherence.
Multi-view-oriente 3D scene generation. A major class of data-driven methods adopts two-stage pipeline: generate multi-view images first, then reconstruct. CAT3D (Gao et al., 2024) synthesizes novel views via multi-view diffusion, followed by 3D reconstruction. DimensionX (Sun al., 2024) generates temporally coherent videos, expands viewpoints through video diffusion, and reconstructs 3D scenes from frames. ODIN (Wallingford et al., 2024) produces trajectory-conditioned novel views
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for subsequent reconstruction. GenXD (Zhao et al., 2025) decouples multi-view and temporal features to jointly generate static and dynamic scenes. Bolt3D (Szymanowicz et al., 2025) outputs colored 3D Gaussians from images and point maps generated by multi-view diffusion. Prometheus ( (Yang et al., 2025) leverages the training paradigm of RGBD latent diffusion models. SplatFlow (Go et al., 2025a) jointly learns camera poses and multi-view image distributions from text. Wonderland (Liang et al., 2025) generates continues multi-view latents via video diffusion, then reconstructs scenes using latent-based reconstruction models. AniGS (Qiu et al., 2025) generates multi-view RGB and normal images and propose strategy to reconstruct 4DGS with inconsistent views.
3D-oriented 3D scene generation. Another line work adopts a 3D-oriented pipeline, employing rendering during denoising steps. DMV3D (Xu et al., 2023) introduces a large iiconstruction-base denoising model based on triplane NeRF representation, performing denoising through NeRF-based reconstruction and rendering. Dual3I> (Li et al., 2024a) proposes a dual-mode multi-view latent diffusion model based on pre-trained image diffusion models and neural surface rendering to reduce training and rendering costs. VideoMV (Zuo et al., 2024) proposes 3D-aware sampling strategy to enhance multi-view consistency during denoising. Director3D (Li et al., 2024b) synthesizes pixel- multi- diffusion,
aligned 3D Gaussians directly from latent space using srajectory-condition followed by SDS++ refinement. DiffusionGS (Cai et al., 2024) presents a diffusion model that outputs pixel-aligned 3DGS each timestep to ensure 3D consistency. Cycle3D (Tang al., 2025) proposes a unified ceneration-reconstrucce
framework, where the 3D reconstruction module is integrated into the multi-step denoising process to further guarantee 3D consistency.
Distillation for diffusion models. Distillation techniques for diffusion models focus on transferring knowledge from pretrained teacher model to more compact and efficient student model. Denoising Student (Luhman & Luhman, 2021) achieves this by training single-step generator to minimize the RMSE between the outputs of the teacher and student models. Consistency Model (Song et al., 2023) enables trajectory distillation, allowing the student to mimic the teacher's denoising process across multiple steps. Adversarial Diffusion Distillation (ADD) (Sauer et al., 2024b), Latent Adversarial Diffusion Distillation (LADD) (Sauer et al., 2024a), Adversarial Post-Trraino 2025a), and Autoregressive Adversarial Post- Training (AAPT) (Lin et al., 2025b) further enhance distillation by introducing adversarial objectives improve the student performance. Distribution Matching Distillation (DMD) (Yin et al., 2024b) formulates the distillation objective as optimizing the reverse KL-divergence between the student and teacher distributions. DMD2 (Yin et al., 2024a) extends this framework by incorporating a GAN-based objective and supporting for multi-step generators, further improving the flexibility and effectiveness of the distillation process.
(APT) (Lin et al.,
D LLM USAGE CLARIFICATION
Large Language Models (LLMs) were utilized solely for language refinement in the preparation of this manuscript. All LL---geeeraed content has been thoroughly reviewed and validated by the authors, who assume full responsibility for the entirety of the paper.
E LIMITATIONS
While the proposed F1 PlashWorld demonstrates strong capabilities in generating high-fidelity and efficient 3D scenes, several limitations remain. First, despite increasing the number of views, the diversity and scale of generated scenes are still constrained by the coverage of existing datasets. Second, the model currently struggles with accurately generating fine-grained geometry, mirror reflections, and articulated objects. These issues may be alleviated by incorporating depth priors (Yang al., 2024a;b; Chen al., 2025) and more 3D-aware structural information (Jiang et al., 2025; Wang et al., 2025) to further enhance the quality of our pixel-aligned 3D Gaussians.
F RGBD RENDERING RESULTS
While Fl Fl FlashWordd d does not explicitly incorporate depth supervision, the 3DGS outputs inherently enable the export of depth maps. In this regard, present several RGBD rendering results in Fig. 9. This serves to demonstrate that our model is capable of learning meaningful depth geometric information solely via image supervision.
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In Fig. 10, show comparison of the depth rendered by our method with that of Director3D and Prometheus. can seen that our model not only produces clearer and more realistic RGB rendering results, but also achieves more accurate depth results.
G COMPARISON TO CAMERA-CONTROLLABLE VIDEO GENERATION MODELS
In the main paper. all core compared baselines capable of generating 3D representations. In Fig. 11, we present a visual comparison of novel view synthesis results between our method and ViewCrafter (Yu et al., 2024b), camera-controllable video generation model. can be observed that the 3DGS rendering results of FlashWorid are comparable in visual quality to the video generation results of VrewCrafter. Additionally, F FlashWorld achieves better color preservation of the input image (e.g.. the identity of the cat in the left column) and outperforms in certain details (e.g., the bushes in the right column). Notably, on one A100 GPU, ViewCrafter requires 2 minutes for generation, while F alasWWrldd only takes 7 seconds. After generating the scene, FlashWorld can perform rendering at real time, while ViewCrafter needs to re-run the diffusion.
H HANDLING OCCLUDED AND TRANSPARENT OBJECTS
For occlusions, F FlashWorll can automatically infer and complete occluded objects by designing appropriate camera positions. We present example in Fig. 12 (bottom) where F FlashWorll ll d han- dles a scenario where most of a car occluded, yet completes it via view synthesis. For transparent objects, since 3DGS employs volume rendering, transparent objects can generally be modeled using low opacity values. We show an example in Fig. (top) where FlashWorls handles a scene inside semi-tramspa semi-transparent fish tank.
I
MORE RESULTS
We provide more generation results in Fig. 13, including object-centrie, indoor, outdoor, realistic, and stylized scenes, to demonstrate the strong and generalizable generation ability of our model.
For video rendering results, please kindly refer to this anonymous website: https: a sc htt.e//cce////aaaato...ddddd//dd Malaaaahoooo_PaaeSPPPaaaa 5FAD/.
Hate Catta
Images
Text
XDGS Parameters
Encoder
Encoder
Cameras
Unprojeccion
Vie Hap Cat
Text Embedkings
3DXS Decoder
Noisy Cass
ConvoD
Crte24
Cameras
Raymaps
ConviD
Reshape
DST Blocks
Reshape
Image Conditions visk 1532 wist C-4E
Conv3D
ConvaD
Prod MV Latenis
H132 Whl
Zero Iribialied
C3222
Figure 8: Architecture of the dual-mode multi-view latent diffusion model.
20

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21
Figure 9: RGBD rendering results.
D3rector3D
Prometheus
Ours
Figure 10: RGBD rendering results of different methods.

---

22
Input
ViewCrafter
Ours
Figure 11: Novel view synthesis results compared to ViewCrafter (Yu et al., 2024b).
Input
Output
Figure 12: FlashWorld is capable of handling occluded and (rams-)transpprent objects.

---

23
Figure 13: More generation results. All images are rendered with generated 3DGS.


Reviews:
[{"review_id": "6ogfbtiLME", "reviewer": "Reviewer_wnKK", "summary": "This paper introduces FlashWorld, a framework for fast and high-quality 3D scene generation that addresses the critical speed-quality trade-off in current methods. The authors propose a cross-mode distillation approach that leverages both MV-oriented (multi-view) and 3D-oriented generation modes. The key innovation lies in using dual-mode pre-training followed by cross-mode post-training, where the MV-oriented mode serves as a teacher to provide visual quality while the 3D-oriented mode acts as a student to ensure geometric consistency.", "questions": "- Does the Out-of-Distribution (OOD) data improve only text-to-3D, or does it also help image-to-3D? The gains for text-to-3D are clear, but the impact on image-to-3D is not.\n- Line 249 is confusing: “we additionally update an MV-oriented student model at a lower frequency.” From Fig. 3, it seems the 3D- and MV-oriented modes share the same DiT backbone, whose latent output feeds the 3DGS decoder. If you update the 3D-oriented model, does that also update the DiT (and therefore the MV-oriented model)? Please clarify which parameters are shared or frozen during training and how updates are applied.\n- How does the method handle scenes with significant occlusions or transparent objects?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The idea of training a dual-mode model and using cross-mode distillation for post-training is creative and well-motivated. Using the MV-oriented mode as teacher for visual quality while training the 3D-oriented mode for consistency is an elegant solution to the speed-quality trade-off.\n\n- Achieving ~9 second generation time while maintaining SOTA quality represents a significant practical contribution. The 10-100× speedup over prior work (CAT3D, Wonderland, etc.) makes this approach much more suitable for real-world applications.", "weaknesses": "- Section 3.2 (cross-mode post-training) needs more detail. This stage appears to integrate DMD2 with the dual-mode diffusion model, but the step-by-step procedure is unclear. Please add a training algorithm box or pseudocode to clarify the process.\n- Include NVS comparisons with video-based methods such as TrajectoryCrafter, GEN3C, and ViewCrafter.\n- Provide depth visualizations of generated scenes and compare against baselines.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VIaWHKe44g", "reviewer": "Reviewer_N491", "summary": "This work presents a generative model that produces high-quality 3D scenes from a single image or text prompt in just seconds—10-100× faster than prior methods. FlashWorld’s key innovation is a distillation strategy that transfers high visual fidelity from a multi-view-oriented diffusion teacher to a 3D-oriented student, improving 3D consistency. The authors also introduce an out-of-distribution co-training strategy to improve generalization to scenes beyond the training distribution. Extensive experiments show FlashWorld outperforms state-of-the-art methods in both generation quality and inference speed.", "questions": "see weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper is clearly written and easy to follow.\n- Experimental validation is thorough, with a comprehensive benchmark against many competing methods.\n- The out-of-distribution co-training approach is an effective and efficient way to improve generalization and robustness.", "weaknesses": "-  Although the model demonstrates strong and generalizable generation capabilities, its video-rendering performance is not extensively evaluated in the main paper.\n- As noted in the limitations, the model still struggles with fine-grained geometry, mirror reflections, and articulated objects.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7Eu2z5w465", "reviewer": "Reviewer_m4Bg", "summary": "The paper tackles scene generation from text and images. Existing video foundation model approaches trade off between multi-view methods, which struggle with 3D consistency, and 3D-focused methods, which often compromise visual fidelity. To overcome this, the authors propose pre-training and post-training techniques for VDMs: a 3DGS decoder (the “3D-oriented” branch) is added during pre-training to inject geometric priors and improve 3D coherence, and a teacher–student distillation step transfers knowledge from a multi-view-oriented teacher to a 3D-oriented student to speed up generation.", "questions": "There may be an error in Eq. (6), where is \\lambda ?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is clearly written and easy to follow.\n\n- The organization is logical, and most design choices are validated with ablation studies.\n\n- The method shows notable improvements over prior work, particularly in preserving fine-grained details.", "weaknesses": "The paper is novel and effective; the following points are offered as constructive suggestions for further improvement.\n\n- The diversity and scale of generated scenes are still constrained by the coverage of existing datasets.\n\n- The model currently struggles with accurately generating fine-grained geometry, mirror reflections, and articulated objects. These issues may be alleviated by incorporating depth priors and more 3D-aware structural information\n\n- Although FlashWorld does not use explicit depth supervision, its 3D Gaussian Splatting (3DGS) outputs can be used to extract depth maps. However, the quality of the resulting depth information could be improved.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Shl3Yt8T1r", "reviewer": "Reviewer_mRkm", "summary": "The paper addresses text- and image-to-scene generation. Previous methods that leverage powerful video foundation models (VDMs) face a trade-off: multi-view-oriented approaches lack 3D consistency, while 3D-oriented methods often yield poor visual quality. This paper introduces pre-training and post-training strategies for VDMs to improve 3D consistency and accelerate video generation. For pre-training, the authors add a 3DGS decoder (the \"3D-oriented\" branch) to inject 3D priors and enhance the latent video diffusion model’s 3D consistency. For post-training, they distill a multi-view-oriented teacher into a 3D-oriented student to speed up generation. Comprehensive experiments show the proposed methods outperform prior state-of-the-art approaches in both qualitative and quantitative evaluations.", "questions": "1. There may be a typo on lines 254--255. You refer to λ in relation to Eq.(6), but Eq.(6) does not contain λ. \n\n2. Missing some relative references:\n- VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model,  which proposes a 3D-aware sampling strategy;\n- AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction, using 4DGS to alleviate the inconsistency from multi-view video generation. \n\nPlease consider add the above relative references.\n\nIn summary, the authors propose interesting pre-training and post-training strategies for video-diffusion model training to improve 3D consistency and quality in scene generation, achieving state-of-the-art results in both quantitative and qualitative experiments. However, the writing flow is poor: some sentences are confusing, and I have concerns about the model’s generalizability. As a result, I am inclined to give a borderline-accept score, though I would be happy to raise it if the authors address these main concerns.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The two proposed training strategies are novel, efficient, and effective for 3D scene generation.\n\n- The authors present comprehensive experiments that convincingly support their claims.\n\n- Comparison results show the proposed method outperforms both image-to-3D and text-to-3D approaches, producing finer-detail renderings and faster generation times.", "weaknesses": "- The writing flow is poor and Sec. 3.2 feels chaotic. For example, the sentence To generate a 3D scene, the 3D-oriented multi-view generation process alternates between denoising and noise injection steps to enhance sample quality.'' is confusing: is the goal to generate a 3D scene, to enhance sample quality, or both? \n\n- For Sec. 3.3, the reason the model improves the quality of out-of-domain (OOD) data is unclear. As I understand it, the approach uses camera-trajectory augmentation to enhance the model’s generalizability and also discards the GAN loss during training. Which of these two changes is more important for OOD performance? Data augmentation is easy to see as a way to improve generalization, but why does including a GAN loss lead to poor generalizability?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)