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REVIEW 2 major objections 4 minor 51 references

CoCo4D: Comprehensive and Complex 4D Scene Generation

T0 review · 2 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read CoCo4D generates wide-view dynamic 4D scenes with moving foreground and background in about one hour, matching or beating slower baselines.

desk verdict A practical 4D scene generation system with real engineering merit, but its main claims need stronger evaluation and the depth composition step is less trustworthy than the paper suggests. read the letter →

arxiv 2506.19798 v1 pith:X3RWQ5HS submitted 2025-06-24 cs.CV

Junwei Zhou , Xueting Li , Lu Qi , Ming-Hsuan Yang This is my paper · ORCID
classification cs.CV
keywords 4Dscenegenerationtext-to-4DGaussianSplattingprogressiveoutpaintingforeground-backgroundcompositionvideoinpaintingdepth-awaremotiontrajectory
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a full 4D scene—a moving foreground object set inside a wide, evolving background—can be generated from a text prompt or a text-plus-image prompt in about one hour, instead of the 4 to 24 hours earlier methods require. It argues that the right decomposition is to treat the foreground and background as separate problems: articulate motion lives in the foreground, gradual motion in the background. The foreground is reconstructed as a static 3D object and then animated, while the background is progressively outpainted into new camera views and animated with a deformation network. If the claim holds, practical users could produce immersive, multi-view-consistent dynamic scenes from a single prompt in about an hour, which is the paper's central case for why the approach matters.

What carries the argument

The load-bearing objects are a foreground 4D Gaussian Splatting model and a background 4D Gaussian Splatting model, each with a deformation network. The background's progressive outpainting loop—render from a new camera, inpaint empty pixels under the guidance of the inpainted first frame, project the inpainted depth back into 3D, then optimize Gaussians with L1, SDS, and total-variation losses—is what expands the camera range. The foreground's parametric trajectory (per-frame shifts $\tau_t$ and scales $s_t$) and the depth-based affine composition of Eqs. 8–10 are what fuse the two layers. The key structural choice is to keep the foreground's static Gaussians frozen during motion learning and to guide the background expansion through an image-inpainting step before video inpainting, which the paper finds makes the outpainted videos temporally consistent.

What would settle it

Render a generated scene from a camera pose substantially outside the reference view at a late timestamp: if the outpainted background repeats texture, tears, or blurs, the wide-range claim fails. A more targeted check is to compare the foreground depth (Eq. 8) with the inpainted background depth at the composition seam; a misalignment larger than roughly one foreground-object width at the reference view would falsify the depth-composition assumption.

Watch

Extended reading notes

Core claim

The central discovery, stated on the paper's own terms, is a two-branch pipeline that keeps the time cost low without sacrificing scene coverage. A reference video is first generated from the input conditions and split into foreground frames, background frames, and masks. The foreground branch reconstructs a 3D Gaussian object, learns a deformation field to match the reference motion, and refines a parametric screen-space trajectory. The background branch projects the first background frame into 3D, then repeatedly renders from new camera poses, inpaints the empty regions using an image-guided video inpainting model, projects the inpainted depth back into the Gaussian scene, and retrains the deformation and Gaussians. The final scene is composed by a depth-aware affine transform that shifts and scales the foreground onto the background coordinate system. The authors report that this pipeline reaches about one hour per scene and that in VBench motion smoothness, dynamic degree, and aesthetic quality it is comparable to or better than the tested baselines.

Load-bearing premise

The monocular depth predictions used for the reference frame and for the background scene are mutually consistent, so the affine composition in Eqs. 8–9 puts the foreground at the correct depth; if those depth maps disagree in scale or alignment, the foreground will float, sink, or misalign with the background.

Editorial extensions

If this is right

  • Text prompts (or text plus a single image) become 4D scenes in about one hour, cutting the time of score-distillation baselines from 13.5–24 hours to roughly one hour.
  • Wide-view backgrounds can be synthesized by iterating small inpaint-project-optimize steps, so scene coverage scales without training a dedicated multi-view video diffusion model.
  • Foreground and background can be optimized with different losses, which is what lets the method combine articulated motion with ambient background motion in one scene.
  • The parametric trajectory gives explicit screen-space control over the foreground path, allowing the motion to be refined or re-targeted during composition.
  • Quantitative metrics from the paper—higher motion smoothness and dynamic degree than the tested baselines—predict that rendered videos will look less jittery and more alive when viewed from novel angles.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An extension the paper does not explore: the progressive outpainting loop is independent of the foreground branch, so it could be reused to expand a static 3D scene from a single monocular image into a wide-view 3D environment.
  • Because the foreground and background are separate Gaussian layers, a natural next step is layer-wise editing—swapping the foreground object or replacing the background prompt without re-running the full pipeline.
  • The explicit trajectory parameters suggest a testable application: re-optimizing only $\{\tau_t, s_t\}$ to move the foreground along a user-drawn path, leaving the background intact.
  • The paper's own limitation note predicts that scenes with strong occlusion or rapid camera motion will stress the depth-composition step; a stress test with such prompts would reveal how often the misalignment appears.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. CoCo4D proposes a text/image-conditioned 4D scene generation framework that decomposes the problem into a dynamic foreground branch and an evolving background branch, guided by a reference video generated from the input. The foreground is reconstructed as static 3D Gaussians, animated with a deformation network, and supervised by the reference video plus Zero123 score distillation. The background is initialized by lifting the first frame to 3D with a monocular depth model, then expanded and animated through a proposed progressive outpainting loop of inpaint-project-optimize steps using image and video inpainting priors. A depth-based affine transform (Eqs. 8--9) places the animated foreground into the background coordinate system, with trajectory shift and scale parameters refined by an RGB alignment loss. The paper claims about one hour of generation time and comparable or superior performance against Dream-in-4D, 4D-fy, and Free4D on a self-curated benchmark.

Significance. The paper addresses a practical gap in 4D scene generation by targeting both wide view range and dynamic background, and the proposed modular pipeline is a plausible way to reduce runtime from tens of hours to about one hour. The work includes useful component ablations, such as the pseudo-video inpainting strategy and depth-inpainting guided projection, and the qualitative results are encouraging. If the depth-alignment assumption in the composition module and the statistical robustness of the quantitative evaluation are confirmed, this would be a meaningful advance for text-to-4D scene synthesis. The reported time cost and the clear separation of foreground/background responsibilities are notable strengths. However, the current evidence for the central 'comparable or superior' claim is not yet convincing because the benchmark is self-curated, no error bars are provided, and the most critical technical step (depth-based composition) is acknowledged in the appendix to be occasionally unreliable without quantitative validation.

major comments (2)
  1. [Sec. 3.3, Eqs. (8)-(9)] The claim that 'since we use the same depth model DM as in background synthesis, the value ranges of two different depth maps are well aligned' is not justified. Monocular depth predictions from the same network on different images are not generally metrically aligned; they are often defined only up to per-image scale and shift. Equation (9) estimates the relative scaling factor from the min/max range of the background region, which is sensitive to outliers and mask errors, and Eq. (8) anchors the foreground with a single scalar depth. The authors themselves state in Appendix D that 'scene composition using depth information is not always precise, misalignment sometimes occurs.' Since this composition step is what integrates the foreground and background into a single coherent 4D scene, the correctness of the affine assumption is load-bearing. I request a quantitative validation of the alignment: for example, depth error at the foreground-background boundary, a seam-consistency metric across rendered views, or an ablation comparing this depth-based composition with an alternative alignment procedure.
  2. [Sec. 4.2, Table 1 and Table 2] The quantitative evaluation supporting the central claim of 'comparable or superior performance' is based on a self-curated set of prompts, with no standardized benchmark, no error bars, and no statistical tests. In Table 1, the CLIP-Score of CoCo4D (23.47%) is slightly lower than that of Dream-in-4D (23.58%), and the reported gains in Motion Smoothness, Dynamic Degree, and Aesthetic Quality cannot be assessed for significance from a single run. The user study in Table 2 also reports no significance testing, no confidence intervals, and no inter-rater reliability. Because these results carry the main claim of the paper, please provide multiple seeds, significance tests or confidence intervals, and release the evaluation prompts, generated 4D scenes, and code so that the comparison is reproducible.
minor comments (4)
  1. [Sec. 4.1 and Appendix A] The number of camera poses is inconsistent: Sec. 4.1 states 'collecting videos from 9 camera poses,' while Appendix A describes 4 poses in the first loop and 4 in the second loop, totaling 8. Please clarify whether the reference view is counted or a pose is shared.
  2. [Sec. 3.2, Eq. (7)] The phrase 'each exc1r1 represents the frame after copy and paste' appears to contain a typo in the subscript; the notation should be cleaned up for readability.
  3. [Appendix B] The text says 'we present the time cost for all current text-to-4D scene generation methods in Fig. 6,' but the corresponding element is Table 6; please correct the cross-reference.
  4. [Sec. 4.2] The user study reports results from 30 users but does not describe the interface, the number of comparisons per user, or whether users were screened for expertise; adding these details would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found; the pipeline is self-supervised with generative priors and the depth-alignment assumption is a robustness limitation, not a circular reduction.

full rationale

CoCo4D is a generative pipeline rather than a derivation of a prediction from first principles. The reference video is an explicit input condition, and the foreground reconstruction, motion optimization, and trajectory refinement are supervised by that reference video (Eq. 3). This is the intended role of the input, not a hidden fitted quantity being relabeled as a prediction. Similarly, the background is extrapolated by inpainting the model's own renderings and using the resulting pseudo-videos as training data; this is self-supervised reuse of generative priors, and the paper does not claim these inpainted videos are independent ground truth. The only questionable step is the depth-affine composition in Eqs. 8-9, where the paper assumes that depth maps from the same monocular model are scale-aligned. That assumption is unverified and could cause the misalignment the authors acknowledge in Appendix D, but it is a correctness and robustness limitation, not a circular reduction: the foreground depth estimate is not defined in terms of the final composed output, and no equation collapses to its own input by construction. The quantitative comparisons are against independently implemented baselines, and no load-bearing argument rests on a self-citation. Therefore the paper does not exhibit the specific input-output equivalence that would constitute circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a stack of pre-trained models and domain assumptions rather than free-standing derivation. The main fitted parameters are the foreground trajectory shift/scale and hand-tuned pipeline constants. No new physical entities, forces, or representations are introduced; the method uses existing 3D/4D Gaussian Splatting and deformation networks.

free parameters (3)
  • Foreground trajectory shift T and scale S = Per-frame 2D shifts and scales, refined with 50 iterations of optimization (Appendix A)
    Optimized to align the foreground with the reference video via the RGB loss (Eq. 3). These are per-scene fitted parameters that directly determine the composed trajectory.
  • Number of outpaint iterations and camera poses = 2 loops with 4 camera poses each in the appendix (Sec. 4.1 mentions 9 poses total, an inconsistency)
    Hand-chosen to balance scene coverage and runtime. No ablation studies varying these counts are provided.
  • Video inpainting strength and SDS noise ratio = strength=0.7, noise ratio=0.5 (Sec. 4.1)
    Hand-tuned constants for the background inpainting and SDS losses; no sensitivity analysis is reported.
assumptions (5)
  • domain assumption Foreground objects exhibit rapid articulated motion while backgrounds change gradually
    Stated in Sec. 1 and used to justify the separate foreground/background pipelines. This fails for scenes with large camera motion or strongly dynamic backgrounds, and is not validated across scene types.
  • domain assumption Monocular depth maps from DM are mutually consistent and appropriate for 3D projection and composition
    Used for background initialization (Sec. 3.2), depth inpainting during outpainting, and foreground-background alignment (Eq. 8-9). The authors list inaccurate depth as a limitation in Appendix D.
  • domain assumption Pre-trained video diffusion models produce reference videos that faithfully realize the text prompt with plausible physics
    The entire pipeline is supervised by this generated reference video. If the video model fails, the 4D scene inherits the failure. No evaluation of video-model failure rates is provided.
  • domain assumption Zero123 multi-view diffusion provides reliable unseen-view supervision for the foreground
    Used in Eq. 2 for SDS on unseen views. The authors note in Appendix D that Zero123 'occasionally produces undesired results'.
  • domain assumption Video inpainting guided by the pseudo video preserves temporal consistency for the extended background
    The pseudo video is created by copy-pasting the inpainted first frame into all frames (Eq. 7). The video inpainting model must propagate this content consistently; no quantitative consistency measurement is reported.

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Pith. "Pith review of CoCo4D: Comprehensive and Complex 4D Scene Generation." pith.science (2026). https://pith.science/paper/X3RWQ5HS

@misc{pith2026250619798,
  author       = {Pith},
  title        = {Pith review of: CoCo4D: Comprehensive and Complex 4D Scene Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X3RWQ5HS}},
  note         = {Machine review of arXiv:2506.19798}
}
read the original abstract

Existing 4D synthesis methods primarily focus on object-level generation or dynamic scene synthesis with limited novel views, restricting their ability to generate multi-view consistent and immersive dynamic 4D scenes. To address these constraints, we propose a framework (dubbed as CoCo4D) for generating detailed dynamic 4D scenes from text prompts, with the option to include images. Our method leverages the crucial observation that articulated motion typically characterizes foreground objects, whereas background alterations are less pronounced. Consequently, CoCo4D divides 4D scene synthesis into two responsibilities: modeling the dynamic foreground and creating the evolving background, both directed by a reference motion sequence. Given a text prompt and an optional reference image, CoCo4D first generates an initial motion sequence utilizing video diffusion models. This motion sequence then guides the synthesis of both the dynamic foreground object and the background using a novel progressive outpainting scheme. To ensure seamless integration of the moving foreground object within the dynamic background, CoCo4D optimizes a parametric trajectory for the foreground, resulting in realistic and coherent blending. Extensive experiments show that CoCo4D achieves comparable or superior performance in 4D scene generation compared to existing methods, demonstrating its effectiveness and efficiency. More results are presented on our website https://colezwhy.github.io/coco4d/.

Figures

Figures reproduced from arXiv: 2506.19798 by the authors.

Figure 1
Figure 1. CoCo4D generates a comprehensive and complex 4D scene with input conditions, achieving [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of CoCo4D. We first generate the reference video (initial motion) for both [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. A simple illustration of the second round of the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of our method against other 4D scene generation methods. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Examples of separated foreground and background 4DGS rendered views in two scenes. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Ablation study on the effectiveness of our pseudo video generation strategy. We show two ation [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Ablation studies on the effectiveness of our depth [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 9
Figure 9. Figure 9: Drawback of PaintScene4D, caused by the inconsistent view-time matrix generated with [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Additional visualization results of our CoCo4D. [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

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Works this paper leans on

51 extracted references · 25 canonical work pages

  1. [1]

    Tc4d: Trajectory-conditioned text-to-4d generation.arXiv preprint arXiv:2403.17920, 2024

    Sherwin Bahmani, Xian Liu, Yifan Wang, Ivan Skorokhodov, Victor Rong, Ziwei Liu, Xihui Liu, Jeong Joon Park, Sergey Tulyakov, Gordon Wetzstein, et al. Tc4d: Trajectory-conditioned text-to-4d generation.arXiv preprint arXiv:2403.17920, 2024. 3

    arXiv 2024

  2. [2]

    Ac3d: Analyzing and improving 3d camera control in video diffusion transformers.arXiv preprint arXiv:2411.18673, 2024

    Sherwin Bahmani, Ivan Skorokhodov, Guocheng Qian, Aliaksandr Siarohin, Willi Menapace, Andrea Tagliasacchi, David B Lindell, and Sergey Tulyakov. Ac3d: Analyzing and improving 3d camera control in video diffusion transformers.arXiv preprint arXiv:2411.18673, 2024. 2

    arXiv 2024

  3. [3]

    Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, and David B. Lindell. 4d-fy: Text-to-4d generation using hybrid score distillation sampling.IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 2, 3, 7

    work page 2024

  4. [4]

    Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023. 3

    arXiv 2023

  5. [5]

    Hexplane: A fast representation for dynamic scenes

    Ang Cao and Justin Johnson. Hexplane: A fast representation for dynamic scenes. InCVPR, 2023. 5

    work page 2023

  6. [6]

    Videocrafter1: Open diffusion models for high-quality video generation, 2023

    Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter1: Open diffusion models for high-quality video generation, 2023. 2

    2023

  7. [7]

    Dreamscene4d: Dynamic multi-object scene generation from monocular videos

    Wen-Hsuan Chu, Lei Ke, and Katerina Fragkiadaki. Dreamscene4d: Dynamic multi-object scene generation from monocular videos. InNeurIPS, 2024. 3

    work page 2024

  8. [8]

    Luciddreamer: Domain-free generation of 3d gaussian splatting scenes.arXiv preprint arXiv:2311.13384, 2023

    Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free generation of 3d gaussian splatting scenes.arXiv preprint arXiv:2311.13384, 2023. 9

    arXiv 2023

Show all 51 references
  1. [9]

    Invisible stitch: Generating smooth 3d scenes with depth inpainting

    Paul Engstler, Andrea Vedaldi, Iro Laina, and Christian Rupprecht. Invisible stitch: Generating smooth 3d scenes with depth inpainting. InArxiv, 2024. 7

    2024

  2. [10]

    Dynamic view synthesis from dynamic monocular video

    Chen Gao, Ayush Saraf, Johannes Kopf, and Jia-Bin Huang. Dynamic view synthesis from dynamic monocular video. InProceedings of the IEEE International Conference on Computer Vision, 2021. 2

    2021

  3. [11]

    Paintscene4d: Consistent 4d scene generation from text prompts.https://arxiv.org/abs/2412.04471, 2024

    Vinayak Gupta, Yunze Man, and Yuxiong Wang. Paintscene4d: Consistent 4d scene generation from text prompts.https://arxiv.org/abs/2412.04471, 2024. 2, 3, 7

    2024 arXiv

  4. [12]

    Animate3d: Animating any 3d model with multi-view video diffusion.arXiv preprint arXiv:2407.11398, 2024

    Yanqin Jiang, Chaohui Yu, Chenjie Cao, Fan Wang, Weiming Hu, and Jin Gao. Animate3d: Animating any 3d model with multi-view video diffusion.arXiv preprint arXiv:2407.11398, 2024. 2

    2024 arXiv

  5. [13]

    Consistent4d: Consistent 360° dynamic object generation from monocular video

    Yanqin Jiang, Li Zhang, Jin Gao, Weiming Hu, and Yao Yao. Consistent4d: Consistent 360° dynamic object generation from monocular video. InThe Twelfth International Conference on Learning Representations,

  6. [14]

    3d gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4), July 2023

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4), July 2023. 4

    2023

  7. [15]

    Text2video-zero: Text-to-image diffusion models are zero-shot video generators

    Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. Text2video-zero: Text-to-image diffusion models are zero-shot video generators. InICCV, 2023. 2

    2023

  8. [16]

    Feng, and Jia-Bin Huang

    Yao-Chih Lee, Yi-Ting Chen, Andrew Wang, Ting-Hsuan Liao, Brandon Y . Feng, and Jia-Bin Huang. Vividdream: Generating 3d scene with ambient dynamics, 2024. 2, 3, 7

    2024

  9. [17]

    Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds.arXiv preprint arXiv:2405.17421, 2024

    Jiahui Lei, Yijia Weng, Adam Harley, Leonidas Guibas, and Kostas Daniilidis. Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds.arXiv preprint arXiv:2405.17421, 2024. 3

    2024 arXiv

  10. [18]

    Craftsman: High-fidelity mesh generation with 3d native generation and interactive geometry refiner.arXiv preprint arXiv:2405.14979, 2024

    Weiyu Li, Jiarui Liu, Rui Chen, Yixun Liang, Xuelin Chen, Ping Tan, and Xiaoxiao Long. Craftsman: High-fidelity mesh generation with 3d native generation and interactive geometry refiner.arXiv preprint arXiv:2405.14979, 2024. 2

    2024 arXiv

  11. [19]

    Feed-forward bullet-time reconstruction of dynamic scenes from monocular videos, 2025

    Hanxue Liang, Jiawei Ren, Ashkan Mirzaei, Antonio Torralba, Ziwei Liu, Igor Gilitschenski, Sanja Fidler, Cengiz Oztireli, Huan Ling, Zan Gojcic, and Jiahui Huang. Feed-forward bullet-time reconstruction of dynamic scenes from monocular videos, 2025. 3

    2025

  12. [20]

    Zero-1-to-3: Zero-shot one image to 3d object

    Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl V ondrick. Zero-1-to-3: Zero-shot one image to 3d object. InICCV, 2023. 4 10

    2023

  13. [21]

    Free4d: Tuning-free 4d scene generation with spatial-temporal consistency

    Tianqi Liu, Zihao Huang, Zhaoxi Chen, Guangcong Wang, Shoukang Hu, liao Shen, Huiqiang Sun, Zhiguo Cao, Wei Li, and Ziwei Liu. Free4d: Tuning-free 4d scene generation with spatial-temporal consistency. arXiv preprint arXiv:2503.20785, 2025. 2, 3, 7

    2025 arXiv

  14. [22]

    Align3r: Aligned monocular depth estimation for dynamic videos.arXiv preprint arXiv:2412.03079, 2024

    Jiahao Lu, Tianyu Huang, Peng Li, Zhiyang Dou, Cheng Lin, Zhiming Cui, Zhen Dong, Sai-Kit Yeung, Wenping Wang, and Yuan Liu. Align3r: Aligned monocular depth estimation for dynamic videos.arXiv preprint arXiv:2412.03079, 2024. 3

    2024 arXiv

  15. [23]

    Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. In2024 International Conference on 3D Vision (3DV), pages 800–809. IEEE, 2024. 4

    2024

  16. [24]

    Benchmark for compositional text-to-image synthesis

    Dong Huk Park, Samaneh Azadi, Xihui Liu, Trevor Darrell, and Anna Rohrbach. Benchmark for compositional text-to-image synthesis. InThirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021. 7

    2021

  17. [25]

    Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023. 7

    2023

  18. [26]

    Dreamfusion: Text-to-3d using 2d diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. ICLR, 2023. 2

    2023

  19. [27]

    Sam 2: Segment anything in images and videos.arXiv preprint arXiv:2408.00714, 2024

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichtenh...

    2024 arXiv

  20. [28]

    Dreamgaussian4d: Generative 4d gaussian splatting.arXiv preprint arXiv:2312.17142, 2023

    Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Generative 4d gaussian splatting.arXiv preprint arXiv:2312.17142, 2023. 4

    2023 arXiv

  21. [29]

    L4gm: Large 4d gaussian reconstruction model.Advances in Neural Information Processing Systems, 37:56828–56858, 2024

    Jiawei Ren, Cheng Xie, Ashkan Mirzaei, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung Wook Kim, Huan Ling, et al. L4gm: Large 4d gaussian reconstruction model.Advances in Neural Information Processing Systems, 37:56828–56858, 2024. 2

    2024

  22. [30]

    Mvdream: Multi-view diffusion for 3d generation.arXiv preprint arXiv:2308.16512, 2023

    Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation.arXiv preprint arXiv:2308.16512, 2023. 2

    2023 arXiv

  23. [31]

    Text-to-4d dynamic scene generation.arXiv preprint arXiv:2301.11280, 2023

    Uriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual, Iurii Makarov, Filippos Kokkinos, Naman Goyal, Andrea Vedaldi, Devi Parikh, Justin Johnson, et al. Text-to-4d dynamic scene generation.arXiv preprint arXiv:2301.11280, 2023. 2, 3

    2023 arXiv

  24. [32]

    Denoising diffusion implicit models, 2022

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models, 2022. 4

    2022

  25. [33]

    Dreamgaussian: Generative gaussian splatting for efficient 3d content creation.ICLR, 2024

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation.ICLR, 2024. 4

    2024

  26. [34]

    Shape of motion: 4d reconstruction from a single video

    Qianqian Wang, Vickie Ye, Hang Gao, Weijia Zeng, Jake Austin, Zhengqi Li, and Angjoo Kanazawa. Shape of motion: 4d reconstruction from a single video. InarXiv preprint arXiv:2407.13764, 2024. 3

    2024

  27. [35]

    Dust3r: Geometric 3d vision made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. InCVPR, 2024. 3

    2024

  28. [36]

    4d gaussian splatting for real-time dynamic scene rendering

    Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 4d gaussian splatting for real-time dynamic scene rendering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 20310–...

  29. [37]

    Zijie Wu, Chaohui Yu, Yanqin Jiang, Chenjie Cao, Fan Wang, and Xiang. Bai. Sc4d: Sparse-controlled video-to-4d generation and motion transfer.arxiv:2404.03736, 2024. 2

    2024 arXiv

  30. [38]

    Structured 3d latents for scalable and versatile 3d generation.arXiv preprint arXiv:2412.01506, 2024

    Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation.arXiv preprint arXiv:2412.01506, 2024. 2

    2024 arXiv

  31. [39]

    Sv4d: Dynamic 3d content generation with multi-frame and multi-view consistency.arXiv preprint arXiv:2407.17470, 2024

    Yiming Xie, Chun-Han Yao, Vikram V oleti, Huaizu Jiang, and Varun Jampani. Sv4d: Dynamic 3d content generation with multi-frame and multi-view consistency.arXiv preprint arXiv:2407.17470, 2024. 1, 2, 3

    2024 arXiv

  32. [40]

    Dynamicrafter: Animating open-domain images with video diffusion priors.arXiv preprint arXiv:2310.12190, 2023

    Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Wangbo Yu, Hanyuan Liu, Xintao Wang, Tien-Tsin Wong, and Ying Shan. Dynamicrafter: Animating open-domain images with video diffusion priors.arXiv preprint arXiv:2310.12190, 2023. 3, 7 11

    arXiv

  33. [41]

    Cogvideox: Text-to-video diffusion models with an expert transformer.arXiv preprint arXiv:2408.06072, 2024

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer.arXiv preprint arXiv:2408.06072, 2024. 3, 7

    2024 arXiv

  34. [42]

    4real: Towards photorealistic 4d scene generation via video diffusion models.Advances in Neural Information Processing Systems, 37:45256–45280, 2024

    Heng Yu, Chaoyang Wang, Peiye Zhuang, Willi Menapace, Aliaksandr Siarohin, Junli Cao, László Jeni, Sergey Tulyakov, and Hsin-Ying Lee. 4real: Towards photorealistic 4d scene generation via video diffusion models.Advances in Neural Information Processing Systems, 37:45256–45280...

    2024

  35. [43]

    Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024

    Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024. 3, 15

    2024 arXiv

  36. [44]

    Trans4d: Realistic geometry-aware transition for compositional text-to-4d synthesis.arXiv preprint arXiv:2410.07155, 2024

    Bohan Zeng, Ling Yang, Siyu Li, Jiaming Liu, Zixiang Zhang, Juanxi Tian, Kaixin Zhu, Yongzhen Guo, Fu-Yun Wang, Minkai Xu, et al. Trans4d: Realistic geometry-aware transition for compositional text-to-4d synthesis.arXiv preprint arXiv:2410.07155, 2024. 3

    arXiv

  37. [45]

    Evaluation agent: Efficient and promptable evaluation framework for visual generative models.arXiv preprint arXiv:2412.09645, 2024

    Fan Zhang, Shulin Tian, Ziqi Huang, Yu Qiao, and Ziwei Liu. Evaluation agent: Efficient and promptable evaluation framework for visual generative models.arXiv preprint arXiv:2412.09645, 2024. 7

    2024 arXiv

  38. [46]

    4diffusion: Multi- view video diffusion model for 4d generation.Advances in Neural Information Processing Systems, 37:15272–15295, 2024

    Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu, Yunhong Wang, and Yu Qiao. 4diffusion: Multi- view video diffusion model for 4d generation.Advances in Neural Information Processing Systems, 37:15272–15295, 2024. 2

    2024

  39. [47]

    Monst3r: A simple approach for estimating geometry in the presence of motion

    Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang. Monst3r: A simple approach for estimating geometry in the presence of motion. arXiv preprint arxiv:2410.03825, 2024. 3

    arXiv

  40. [48]

    Animate124: Animating one image to 4d dynamic scene.arXiv preprint arXiv:2311.14603, 2023

    Yuyang Zhao, Zhiwen Yan, Enze Xie, Lanqing Hong, Zhenguo Li, and Gim Hee Lee. Animate124: Animating one image to 4d dynamic scene.arXiv preprint arXiv:2311.14603, 2023. 2

    2023 arXiv

  41. [49]

    A unified approach for text- and image-guided 4d scene generation

    Yufeng Zheng, Xueting Li, Koki Nagano, Sifei Liu, Otmar Hilliges, and Shalini De Mello. A unified approach for text- and image-guided 4d scene generation. InCVPR, 2024. 3, 7

    2024

  42. [50]

    Layout-your-3d: Controllable and precise 3d generation with 2d blueprint.ICLR, 2025

    Junwei Zhou, Xueting Li, Lu Qi, and Ming-Hsuan Yang. Layout-your-3d: Controllable and precise 3d generation with 2d blueprint.ICLR, 2025. 2

    2025

  43. [51]

    The astronaut slowly standing up on a rocky planet, with a bright planet and stars twinkling behind him

    Bojia Zi, Shihao Zhao, Xianbiao Qi, Jianan Wang, Yukai Shi, Qianyu Chen, Bin Liang, Kam-Fai Wong, and Lei Zhang. Cococo: Improving text-guided video inpainting for better blue consistency, controllability and compatibility.arXiv preprint arXiv:2403.12305, 2024. 2, 5, 7 12 Appe...

    2024 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.