Pith. sign in

REVIEW 4 major objections 4 minor 1 cited by

AR4D: Autoregressive 4D Generation from Monocular Videos

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read AR4D claims state-of-the-art 4D generation from a monocular video without score distillation sampling, by deforming 3D Gaussians frame by frame under progressive pseudo-view supervision.

desk verdict A credible SDS-free 4D generation pipeline with a real soft spot: the LGM pseudo-view supervision is load-bearing and unvalidated, but the method is clearly described and the ablations justify sending it to review. read the letter →

arxiv 2501.01722 v1 pith:AX6NLTZU submitted 2025-01-03 cs.CV

Hanxin Zhu , Tianyu He , Xiqian Yu , Junliang Guo , Zhibo Chen , Jiang Bian This is my paper · ORCID
classification cs.CV
keywords 4Dgenerationscoredistillationsamplingmonocularvideo3DGaussiansplattingautoregressivedeformationfieldprogressiveviewnovelsynthesis
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 argues that dynamic 3D asset creation from a single fixed-viewpoint video does not need Score Distillation Sampling. Instead, it builds a canonical 3D Gaussian representation of the first frame, then generates each later frame's 3D representation from the previous one through a local deformation field, using progressively widened pseudo-views from a pre-trained reconstruction model to keep the geometry from overfitting to one camera angle. A final global deformation stage, constrained by per-frame depth, removes the appearance drift that accumulates along the chain. On the benchmark used by the previous strongest baseline, AR4D reports the highest PSNR, SSIM, and CLIP similarity and the lowest LPIPS and FVD among the compared methods, for both video-to-4D and text-to-4D. If the claim holds, 4D generation becomes a reconstruction-driven pipeline whose diversity and consistency come from the input and the pseudo-view supervisor, not from a diffusion prior.

What carries the argument

The load-bearing mechanism is a cascade of deformation fields over 3D Gaussians. A per-frame local field $F_{\theta_i}(\gamma(\mu_i))$ predicts position, opacity, and scale changes from frame $i$ to frame $i+1$, keeping rotations and colors tied to the previous frame; a progressive view sampling schedule expands the sampled azimuth range from $N_{\mathrm{start}}=1$ to $N_{\max}=180$ through $N_u=\min(N_{\max}, \lfloor u/\eta\rfloor+N_{\mathrm{start}})$, with pseudo-Gaussians from a pre-trained reconstruction model supplying RGB and depth losses; and a global field $F_\theta(\gamma(\mu_1), k)$ re-expresses every frame from the canonical first-frame Gaussians while per-frame depth maps hold the geometry fixed. This turns an underconstrained monocular sequence into a series of locally supervised 3D reconstructions followed by one drift-correcting alignment.

What would settle it

Run the generation stage with a reconstruction model whose depth output has been deliberately corrupted on a held-out set; if final video-to-4D quality degrades roughly as much as when the pseudo-view loss is removed entirely, the reported consistency gains are carried by pseudo-view reliability rather than by the autoregressive mechanism. A simpler check is to compare the generated novel-view FVD against two different pre-trained reconstruction models on the same videos.

Watch

Extended reading notes

Core claim

AR4D's central discovery is that autoregressive 3D reconstruction, rather than score distillation, can carry monocular video-to-4D generation. Each pair of adjacent frames is connected by an independent deformation field over 3D Gaussians, and the underdetermined single-view supervision is resolved by pseudo-views that the pipeline generates itself: it renders the current Gaussians, reconstructs pseudo-Gaussians with a large pre-trained reconstruction model, and supervises RGB and depth at an azimuth range that grows during optimization. The reported results are a PSNR of 31.00 versus 29.91 for the best prior baseline on video-to-4D, with FVD falling from 737 to 617, and analogous improvements on text-to-4D. The paper interprets these numbers as evidence that SDS is not a necessary component for state-of-the-art 4D generation.

Load-bearing premise

The method assumes the pre-trained model that produces synthetic extra camera views, especially its depth estimates, is trustworthy for the test videos; the original frames alone cannot detect when those synthetic views are systematically wrong.

Editorial extensions

If this is right

  • Monocular fixed-viewpoint videos, including videos generated from text, become directly usable inputs for 4D asset creation without optimizing against a diffusion model.
  • The quality and diversity of the output inherit the input video and the reconstruction prior, so the pipeline sidesteps the over-saturation and blurriness the paper attributes to SDS.
  • Improvements in single-image 3D reconstruction models should translate directly into improvements in 4D generation, since the pseudo-view supervisor is the main consistency signal.
  • Removing the progressive pseudo-view supervision in the ablation raises FVD from 617 to 1637, so the consistency gain is carried by pseudo-views, not by the autoregressive chain alone.
  • Text-to-4D reduces to text-to-video followed by this three-stage reconstruction, potentially reusing existing video generators as front-ends.

Reading between the lines

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

  • An unstated corollary is that the same autoregressive-plus-pseudo-view recipe may transfer to other dynamic 3D representations, but the paper only demonstrates it for 3D Gaussians with fixed topology inherited from the first frame.
  • A testable extension is to swap the pre-trained reconstruction model for a stronger or weaker one and measure the FVD gap: if the gap tracks the reconstruction model's depth accuracy, the method's ceiling is set by pseudo-view reliability rather than by the autoregressive design.
  • The depth-stability assumption used in the refinement stage will likely fail on scenes with topological changes or abrupt lighting shifts, which the paper does not test.
  • An independent evaluation on unseen prompts and held-out videos would be needed to confirm that the reported benchmark margins generalize beyond the provided dataset.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes AR4D, a three-stage SDS-free pipeline for 4D generation from a monocular fixed-view video. Stage 1 initializes a 3D Gaussian representation of the first frame from MVDream+LGM and fine-tunes it against the reference frame. Stage 2 generates each subsequent frame autoregressively with a per-pair local deformation MLP, regularized by progressively sampled novel views supervised with pseudo-Gaussians produced by LGM. Stage 3 fits a global deformation field from the canonical first frame to the per-frame Gaussians, constrained by reference views and depth maps, to reduce appearance drift. Experiments on STAG4D-style datasets report higher PSNR/SSIM/LPIPS/CLIP-S/FVD than Consistent4D, SV4D, and STAG4D, plus ablations of each stage.

Significance. AR4D addresses a real limitation of SDS-based 4D generation and the pipeline is clearly structured; if the empirical claims hold, it is a useful SDS-free alternative. Strengths: the three-stage design is intuitive, the ablation table supports each component, and the method avoids SDS by construction. The main risk is that the strongest claims (state-of-the-art quality, consistency, diversity) rest on evidence that is currently incomplete: the LGM pseudo-view supervision is not validated, the quantitative comparisons have no variance or significance analysis, and the diversity claim is not measured. The central idea is defensible and the issues are fixable, so the paper merits revision rather than rejection.

major comments (4)
  1. [Sec. 4.2, Eqs. (5) and (7)] The optimization variables are listed as {θi, µi, αi, si, ri, ci}, i.e., the local deformation field and the parameters of the previous frame's Gaussians Gi, rather than the current frame's Gi+1. If Gi is intentionally optimized, the process is not strictly autoregressive and earlier frames can change after they have been emitted, which is relevant to the claimed temporal consistency; if Gi is meant to be frozen, the equations should list {θi, µi+1, αi+1, si+1, ri+1, ci+1} or state the constraint. As written, the central formulation is ambiguous.
  2. [Sec. 4.2, Eqs. (7)-(8)] The reliability of LGM pseudo-views is load-bearing because these are the only non-reference-view supervisions during generation, and LGM is also used in Sec. 4.1 to construct the canonical Gaussians. The sentence 'we find can guarantee the spatial-temporal consistency' is not an error analysis. Please provide quantitative evidence of LGM's pseudo-view accuracy on the test distribution, for example an oracle ablation that replaces pseudo-views with ground-truth multi-view renders, error maps on held-out views, or a discussion of systematic LGM bias and its propagation through the progressive sampling.
  3. [Sec. 5.1, Tables 1 and 2] The state-of-the-art claim rests on a single set of numbers per method with no error bars, significance tests, or number of test sequences, and the dataset is only described as 'the provided datasets' from STAG4D. In addition, the computation of CLIP-S and FVD is not defined: which views are compared, over how many frames, and with what sampling. Without this information, the reported improvements (e.g., PSNR 31.00 vs. 29.91) cannot be assessed as significant or reproduced.
  4. [Sec. 5.4 and abstract] The claim of 'greater diversity' is not supported by any experiment or metric in the paper; Tables 1-2 and the ablations measure fidelity and consistency, not diversity. Either add a diversity evaluation (e.g., distributional distance over multiple generated samples or a user study) or qualify the claim to match the evidence actually presented.
minor comments (4)
  1. [Sec. 5.1] The text says the experiments cover 'video-to-4D and image-to-4D tasks', but Table 2 is labeled 'Text-to-4D'; please reconcile the terminology and describe how the text-to-4D inputs are constructed.
  2. [Sec. 4.2, Eq. (7)] Equation (7) writes the objective as lrgb + ldepth, while Fig. 2 and the surrounding text indicate that lref is also part of the total loss; the equation should be clarified to show the full objective.
  3. [Sec. 5.1] Hyperparameters such as λ=0.8, η=10, Nstart=1, Nmax=180, and the 2000-iteration setting are given without any sensitivity analysis; the progressive view sampling behavior depends directly on these choices, so a brief sensitivity study would improve confidence.
  4. [Throughout] There are minor typographical issues (e.g., 'refered' in Sec. 1, 'N uincreases' in Sec. 4.2) and the figure placement for Figs. 6-7 is not aligned with the metric tables; a careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation is an external-prior-regularized per-scene optimization, and the SOTA claims are benchmarked against external ground truth.

full rationale

The claimed derivation chain is an optimization pipeline rather than an analytical prediction that reduces to its own inputs. The canonical space is built from external pre-trained models (MVDream and LGM) and fine-tuned against the reference frame (Eq. 2). Each subsequent frame is obtained by local deformation fields optimized against the input reference frames (Eq. 5) and against pseudo-views produced by the fixed, external LGM model from renderings of the current estimate (Eqs. 7-8). Although the pseudo-view target is derived from renderings of the current Gaussians, the target is produced by an external pre-trained model, making this a self-distillation regularizer rather than a definitional reduction; any systematic LGM error is a robustness and correctness concern, not a circularity. The refinement stage (Eqs. 9-11) uses the already-generated per-frame geometry as constraints with the first-frame Gaussians as canonical space, which is an architectural choice and not a circular prediction. The central state-of-the-art claim is supported by quantitative comparisons on the external STAG4D benchmark (Tables 1-2) against external baselines, so it does not reduce to fitted constants or to the paper's own assumptions. The only self-citation ([58]) appears in a list of SDS-based methods and is not load-bearing. No step satisfies the evidentiary bar for circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical or representational entities are introduced. All quantities (3D Gaussians, deformation MLPs, depth maps) are standard in the 3DGS and dynamic-scene literature; the method is a pipeline that combines existing representations and pre-trained models.

free parameters (4)
  • lambda (L1/SSIM weight) = 0.8
    Balancing weight between L1 and SSIM in the reference loss (Eq. 5). Hand-chosen; no sensitivity analysis reported.
  • eta (view range growth rate) = 10
    Controls how fast the sampled azimuth range expands in Eq. (6). Hand-chosen; affects how quickly pseudo-views expand.
  • Nstart and Nmax (azimuth limits) = 1 and 180
    Initial and maximum absolute azimuth angles in progressive view sampling, Eq. (6). Hand-chosen; no study of their effect is provided.
  • training iterations = 2000 per local field, 30000 for global field
    Optimization length for each deformation MLP. Hand-chosen; no convergence or sensitivity analysis.
assumptions (4)
  • standard math 3D Gaussian Splatting provides a differentiable rendering of the scene (Eq. 1).
    The entire method builds on the 3DGS representation and rasterization from Kerbl et al. [12].
  • domain assumption Pre-trained models MVDream, LGM, and Splatt3R provide sufficiently reliable 3D priors from a single frame or sparse views.
    Initialization (Sec 4.1) and pseudo-view generation (Sec 4.2) depend on the correctness of these external models; no error analysis is given in the paper.
  • domain assumption Geometry of dynamic objects is low-frequency and stable across time, so depth maps can constrain deformation.
    The refinement stage (Sec 4.3) relies on the RegNeRF [26] finding that geometry is relatively stable; this may fail for rapidly deforming scenes.
  • domain assumption Motion between adjacent frames is small enough to be modeled by a local MLP deformation field.
    Sec 4.2 assumes the autoregressive nature of videos, meaning each frame is a minor deformation of its predecessor.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AR4D: Autoregressive 4D Generation from Monocular Videos." pith.science (2026). https://pith.science/paper/AX6NLTZU

@misc{pith2026250101722,
  author       = {Pith},
  title        = {Pith review of: AR4D: Autoregressive 4D Generation from Monocular Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AX6NLTZU}},
  note         = {Machine review of arXiv:2501.01722}
}
read the original abstract

Recent advancements in generative models have ignited substantial interest in dynamic 3D content creation (\ie, 4D generation). Existing approaches primarily rely on Score Distillation Sampling (SDS) to infer novel-view videos, typically leading to issues such as limited diversity, spatial-temporal inconsistency and poor prompt alignment, due to the inherent randomness of SDS. To tackle these problems, we propose AR4D, a novel paradigm for SDS-free 4D generation. Specifically, our paradigm consists of three stages. To begin with, for a monocular video that is either generated or captured, we first utilize pre-trained expert models to create a 3D representation of the first frame, which is further fine-tuned to serve as the canonical space. Subsequently, motivated by the fact that videos happen naturally in an autoregressive manner, we propose to generate each frame's 3D representation based on its previous frame's representation, as this autoregressive generation manner can facilitate more accurate geometry and motion estimation. Meanwhile, to prevent overfitting during this process, we introduce a progressive view sampling strategy, utilizing priors from pre-trained large-scale 3D reconstruction models. To avoid appearance drift introduced by autoregressive generation, we further incorporate a refinement stage based on a global deformation field and the geometry of each frame's 3D representation. Extensive experiments have demonstrated that AR4D can achieve state-of-the-art 4D generation without SDS, delivering greater diversity, improved spatial-temporal consistency, and better alignment with input prompts.

Figures

Figures reproduced from arXiv: 2501.01722 by the authors.

Figure 1
Figure 1. Illustration of autoregressive 4D generation. In com￾parison to SDS-based methods (e.g., Consistent4D [11]), our ap￾proach enables SDS-free 4D generation with substantial advance￾ments, including better alignment with input videos and improved spatial-temporal consistency, etc. ages [25, 27, 33, 55] and videos [5, 40, 44, 53] from sim￾ple prompts. Building on these successes, numerous stud￾ies have sought to extend … view at source ↗
Figure 2
Figure 2. Paradigm of our proposed AR4D. To enable SDS-free 4D generation, we propose a three-stage approach consisting of Initial￾ization, Generation, and Refinement. Please see Sec. 4 for more details. techniques to produce corresponding 4D assets. To expe￾dite the generation process, L4GM [32] introduced the first 4D Large Reconstruction Model capable of producing ani￾mated objects in a single feed-forward pass within just… view at source ↗
Figure 3
Figure 3. Ablation studies on finetuning the 3D Gaussians in the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Ablation studies on whether applying autoregressive 4D [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Results of the Refinement stage demonstrate its effec￾tiveness in addressing appearance drift. While appearance may fluctuate, the geometry (evident in the consistent depth map) re￾mains stable, enabling the generation of spatial-temporal consis￾tent 4D contents. Speci…
Figure 6
Figure 6. Figure 6: Comparisons of our proposed AR4D with other state [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Comparisons of our proposed AR4D with other state-of [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: Ablation study on the effect of autoregressive genera [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 8
Figure 8. Figure 8: Comparisons of our proposed AR4D with Deform [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    AniGS animates a static 3D Gaussian Splatting scene by iteratively distilling video-diffusion motion into a time-conditioned deformation field while keeping static regions fixed.

Reference graph

Works this paper leans on

58 extracted references · 18 canonical work pages · cited by 1 Pith paper

  1. [1]

    Hyperreel: High-fidelity 6-dof video with ray- conditioned sampling

    Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes Kopf, Matthew O’Toole, and Changil Kim. Hyperreel: High-fidelity 6-dof video with ray- conditioned sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16610–16620, 2023. 2

    work page 2023

  2. [2]

    4d-fy: Text-to-4d generation using hybrid score dis- tillation sampling

    Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, and David B Lin- dell. 4d-fy: Text-to-4d generation using hybrid score dis- tillation sampling. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 7996–8006, 2024. 1, 3

    work page 2024

  3. [3]

    Vd3d: Taming large video diffu- sion transformers for 3d camera control

    Sherwin Bahmani, Ivan Skorokhodov, Aliaksandr Siaro- hin, Willi Menapace, Guocheng Qian, Michael Vasilkovsky, Hsin-Ying Lee, Chaoyang Wang, Jiaxu Zou, Andrea Tagliasacchi, et al. Vd3d: Taming large video diffu- sion transformers for 3d camera control. arXiv preprint arXiv:2407.12781, 2024. 3

    arXiv 2024

  4. [4]

    Tc4d: Trajectory-conditioned text-to-4d generation

    Sherwin Bahmani, Xian Liu, Wang Yifan, Ivan Sko- rokhodov, Victor Rong, Ziwei Liu, Xihui Liu, Jeong Joon Park, Sergey Tulyakov, Gordon Wetzstein, et al. Tc4d: Trajectory-conditioned text-to-4d generation. In European Conference on Computer Vision , pages 53–72. Springer,

    work page

  5. [5]

    Stable video diffusion: Scaling latent video diffusion models to large datasets

    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. 1, 2

    arXiv 2023

  6. [6]

    Objaverse: A universe of annotated 3d objects

    Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142–13153, 2023. 2

    2023

  7. [7]

    Gaussianflow: Splatting gaussian dynamics for 4d content creation

    Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall, Wen- chao Ma, Le Chen, Danhang Tang, and Ulrich Neumann. Gaussianflow: Splatting gaussian dynamics for 4d content creation. arXiv preprint arXiv:2403.12365, 2024. 1, 3

    arXiv 2024

  8. [8]

    Cameractrl: Enabling camera control for text-to-video generation

    Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation. arXiv preprint arXiv:2404.02101, 2024. 3

    arXiv 2024

Show all 58 references
  1. [9]

    Training-free camera control for video generation

    Chen Hou, Guoqiang Wei, Yan Zeng, and Zhibo Chen. Training-free camera control for video generation. arXiv preprint arXiv:2406.10126, 2024. 3

    2024 arXiv

  2. [10]

    Animate3d: Animating any 3d model with multi-view video diffusion

    Yanqin Jiang, Chaohui Yu, Chenjie Cao, Fan Wang, Weim- ing Hu, and Jin Gao. Animate3d: Animating any 3d model with multi-view video diffusion. arXiv preprint arXiv:2407.11398, 2024. 1, 3

    2024 arXiv

  3. [11]

    Consistent4d: Consistent 360 {\deg} dynamic object gener- ation from monocular video

    Yanqin Jiang, Li Zhang, Jin Gao, Weimin Hu, and Yao Yao. Consistent4d: Consistent 360 {\deg} dynamic object gener- ation from monocular video. The Twelfth International Con- ference on Learning Representations, 2024. 1, 2, 3, 6, 7

    2024

  4. [12]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1,

  5. [13]

    Vivid-zoo: Multi-view video generation with diffusion model

    Bing Li, Cheng Zheng, Wenxuan Zhu, Jinjie Mai, Biao Zhang, Peter Wonka, and Bernard Ghanem. Vivid-zoo: Multi-view video generation with diffusion model. arXiv preprint arXiv:2406.08659, 2024. 2

    2024 arXiv

  6. [14]

    Dreammesh4d: Video-to-4d generation with sparse-controlled gaussian-mesh hybrid representation

    Zhiqi Li, Yiming Chen, and Peidong Liu. Dreammesh4d: Video-to-4d generation with sparse-controlled gaussian-mesh hybrid representation. arXiv preprint arXiv:2410.06756, 2024. 1, 3

    2024 arXiv

  7. [15]

    Spacetime gaus- sian feature splatting for real-time dynamic view synthesis

    Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. Spacetime gaus- sian feature splatting for real-time dynamic view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8508–8520, 2024. 2

    2024

  8. [16]

    Diffusion4d: Fast spatial-temporal consis- tent 4d generation via video diffusion models.arXiv preprint arXiv:2405.16645, 2024

    Hanwen Liang, Yuyang Yin, Dejia Xu, Hanxue Liang, Zhangyang Wang, Konstantinos N Plataniotis, Yao Zhao, and Yunchao Wei. Diffusion4d: Fast spatial-temporal consis- tent 4d generation via video diffusion models.arXiv preprint arXiv:2405.16645, 2024. 2

    2024 arXiv

  9. [17]

    Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching

    Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiao- gang Xu, and Yingcong Chen. Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 6517–6526, 20...

    2024

  10. [18]

    Align your gaussians: Text-to-4d with dynamic 3d gaussians and composed diffusion models

    Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fi- dler, and Karsten Kreis. Align your gaussians: Text-to-4d with dynamic 3d gaussians and composed diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8576–8588, 2024. 1, 3

    2024

  11. [19]

    One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimiza- tion

    Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimiza- tion. Advances in Neural Information Processing Systems , 36, 2024. 1

    2024

  12. [20]

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

    Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tok- makov, Sergey Zakharov, and Carl V ondrick. Zero-1-to- 3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023. 1

    2023

  13. [21]

    Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age

    Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age. arXiv preprint arXiv:2309.03453, 2023. 2 9

    2023 arXiv

  14. [22]

    Wonder3d: Sin- gle image to 3d using cross-domain diffusion

    Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Sin- gle image to 3d using cross-domain diffusion. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pa...

    2024

  15. [23]

    Pla4d: Pixel-level alignments for text-to-4d gaussian splatting

    Qiaowei Miao, Yawei Luo, and Yi Yang. Pla4d: Pixel-level alignments for text-to-4d gaussian splatting. arXiv preprint arXiv:2405.19957, 2024. 1, 3

    2024 arXiv

  16. [24]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 3

    2021

  17. [25]

    T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

    Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 4296–4304, 2024. 1

    2024

  18. [26]

    Reg- nerf: Regularizing neural radiance fields for view synthesis from sparse inputs

    Michael Niemeyer, Jonathan T Barron, Ben Mildenhall, Mehdi SM Sajjadi, Andreas Geiger, and Noha Radwan. Reg- nerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition,...

    2022

  19. [27]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 1

    2023 arXiv

  20. [28]

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

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Milden- hall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 2

    2022 arXiv

  21. [29]

    D-nerf: Neural radiance fields for dynamic scenes

    Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 10318–10327, 2021. 2

    2021

  22. [30]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  23. [31]

    Dreamgaussian4d: Genera- tive 4d gaussian splatting

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

    arXiv

  24. [32]

    L4gm: Large 4d gaus- sian reconstruction model

    Jiawei Ren, Kevin Xie, Ashkan Mirzaei, Hanxue Liang, Xi- aohui Zeng, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung Wook Kim, et al. L4gm: Large 4d gaus- sian reconstruction model. arXiv preprint arXiv:2406.10324,

    arXiv

  25. [33]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 1

    2022

  26. [34]

    Mvdream: Multi-view diffusion for 3d gen- eration

    Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d gen- eration. arXiv preprint arXiv:2308.16512, 2023. 2, 7

    2023 arXiv

  27. [35]

    Splatt3r: Zero-shot gaussian splat- ting from uncalibarated image pairs

    Brandon Smart, Chuanxia Zheng, Iro Laina, and Vic- tor Adrian Prisacariu. Splatt3r: Zero-shot gaussian splat- ting from uncalibarated image pairs. arXiv preprint arXiv:2408.13912, 2024. 2, 7

    2024 arXiv

  28. [36]

    Eg4d: Explicit generation of 4d object without score distil- lation

    Qi Sun, Zhiyang Guo, Ziyu Wan, Jing Nathan Yan, Sheng- ming Yin, Wengang Zhou, Jing Liao, and Houqiang Li. Eg4d: Explicit generation of 4d object without score distil- lation. arXiv preprint arXiv:2405.18132, 2024. 1

    2024 arXiv

  29. [37]

    Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation. arXiv preprint arXiv:2309.16653,

    arXiv

  30. [38]

    Lgm: Large multi-view gaussian model for high-resolution 3d content creation

    Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision, pages 1–18. Springer, 2024. 2, 5, 7

    2024

  31. [39]

    To- wards accurate generative models of video: A new metric & challenges

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. To- wards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717, 2018. 7

    2018 arXiv

  32. [40]

    Phenaki: Variable length video generation from open domain textual descriptions

    Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kin- dermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length video generation from open domain textual descriptions. In International Conference on Le...

    2022

  33. [41]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004. 7

    2004

  34. [42]

    Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion. Advances in Neural Information Processing Systems , 36, 2024. 2, 3, 8

    2024

  35. [43]

    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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20310–20320, 2024. 2

    2024

  36. [44]

    Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

    Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. In Proceedings of the IEEE/CVF International Conference...

    2023

  37. [45]

    Sv4d: Dynamic 3d content generation with multi-frame and multi-view consistency

    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. 2, 6, 7

    2024 arXiv

  38. [46]

    Camco: Camera- 10 controllable 3d-consistent image-to-video generation

    Dejia Xu, Weili Nie, Chao Liu, Sifei Liu, Jan Kautz, Zhangyang Wang, and Arash Vahdat. Camco: Camera- 10 controllable 3d-consistent image-to-video generation. arXiv preprint arXiv:2406.02509, 2024. 3

    2024 arXiv

  39. [47]

    Deformable 3d gaussians for high- fidelity monocular dynamic scene reconstruction

    Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high- fidelity monocular dynamic scene reconstruction. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20331–20341, 2024. 2, 4, 7, 8

    2024

  40. [48]

    Diffu- sion 2: Dynamic 3d content generation via score com- position of orthogonal diffusion models

    Zeyu Yang, Zijie Pan, Chun Gu, and Li Zhang. Diffu- sion 2: Dynamic 3d content generation via score com- position of orthogonal diffusion models. arXiv preprint arXiv:2404.02148, 2024. 1

    2024 arXiv

  41. [49]

    Gaussian- dreamer: Fast generation from text to 3d gaussian splatting with point cloud priors

    Taoran Yi, Jiemin Fang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussian- dreamer: Fast generation from text to 3d gaussian splatting with point cloud priors. arXiv preprint arXiv:2310.08529 ,

    arXiv

  42. [50]

    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

    2024 arXiv

  43. [51]

    4dynamic: Text-to-4d gen- eration with hybrid priors

    Yu-Jie Yuan, Leif Kobbelt, Jiwen Liu, Yuan Zhang, Pengfei Wan, Yu-Kun Lai, and Lin Gao. 4dynamic: Text-to-4d gen- eration with hybrid priors. arXiv preprint arXiv:2407.12684,

    arXiv

  44. [52]

    Stag4d: Spatial-temporal anchored generative 4d gaussians

    Yifei Zeng, Yanqin Jiang, Siyu Zhu, Yuanxun Lu, Youtian Lin, Hao Zhu, Weiming Hu, Xun Cao, and Yao Yao. Stag4d: Spatial-temporal anchored generative 4d gaussians. In Eu- ropean Conference on Computer Vision , pages 163–179. Springer, 2025. 1, 2, 3, 6, 7

    2025

  45. [53]

    Show-1: Marrying pixel and latent diffusion models for text-to-video generation

    David Junhao Zhang, Jay Zhangjie Wu, Jia-Wei Liu, Rui Zhao, Lingmin Ran, Yuchao Gu, Difei Gao, and Mike Zheng Shou. Show-1: Marrying pixel and latent diffusion models for text-to-video generation. International Journal of Com- puter Vision, pages 1–15, 2024. 1

    2024

  46. [54]

    4diffusion: Multi-view video diffusion model for 4d generation

    Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu, Yunhong Wang, and Yu Qiao. 4diffusion: Multi-view video diffusion model for 4d generation. arXiv preprint arXiv:2405.20674, 2024. 2

    2024 arXiv

  47. [55]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 1

    2023

  48. [56]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 586–595, 2018. 7

    2018

  49. [57]

    Animate124: Animating one im- age to 4d dynamic scene

    Yuyang Zhao, Zhiwen Yan, Enze Xie, Lanqing Hong, Zhen- guo Li, and Gim Hee Lee. Animate124: Animating one im- age to 4d dynamic scene. arXiv preprint arXiv:2311.14603,

    arXiv

  50. [58]

    Compositional 3d-aware video gen- eration with llm director

    Hanxin Zhu, Tianyu He, Anni Tang, Junliang Guo, Zhibo Chen, and Jiang Bian. Compositional 3d-aware video gen- eration with llm director. arXiv preprint arXiv:2409.00558,

    arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.