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REVIEW 3 major objections 4 minor 53 references

Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Restage4D claims that rewinding a real video and splicing it to a generated one allows one shared motion representation to propagate real geometry into novel synthetic motion, preserving deformable structure and correcting generative artifa

desk verdict The video-rewinding idea is genuinely clever, but the core lemma is unproven and the evaluation is too thin to support the strong claims. read the letter →

arxiv 2508.06715 v1 pith:W4L2PP3F submitted 2025-08-08 cs.CV

Jixuan He , Chieh Hubert Lin , Lu Qi , Ming-Hsuan Yang This is my paper
classification cs.CV
keywords 4Drestagingdeformable3DreconstructionGaussiansplattingvideoconditioningmotionretargetingdiffusionmodelsocclusion-awarerigiditydisocclusionbacktracing
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

This paper is trying to establish that a single real video can serve as the physical anchor for reanimating a scene with new synthetic motion. The idea is to play the real video backward, splice it to a generated driving video that starts at the same first frame, and reconstruct the concatenation as one 4D Gaussian scene with shared low-rank motion coefficients; the real clip's geometry then propagates into the synthetic segment. The paper claims this preserves deformable structure, improves 3D tracking and occlusion consistency, and automatically repairs artifacts produced by image-to-video diffusion models, including infeasible deformation and inconsistent geometry. If correct, it makes text-driven 4D content creation controllable by real physics while staying diverse, because any number of driving videos can pull the strings of the same reconstructed puppet.

What carries the argument

Low-rank decomposed 4D Gaussian representation: each dynamic foreground point has canonical position $\mu_i$, shared time-independent coefficients $\beta_i \in \mathbb{R}^K$, and $K$ smooth time-varying SE(3) bases $M_k(t)$; deformation is $T_i(t)=\gamma(\sum_k \beta_{ik}M_k(t))$ applied to $\mu_i$. Shared coefficients let the rewound real clip and the driving clip express one articulation, so real-video supervision flows into the edited segment. Supporting it are the video-rewinding concatenation, the occlusion-aware rigidity loss acting on an invisibility score, and disocclusion backtracing.

What would settle it

On a PointOdyssey clip with ground-truth 3D tracks, reconstruct the base clip forward and backward under the same low-rank representation and compare per-point canonical positions; then run the joint rewind+driving training and measure $\mathrm{Var}_{t\in[0,t_1]}(d(t))$ against a model trained on the edited clip alone. If the forward/backward reconstructions differ beyond tracking noise, or if the joint-training variance is not below the edited-only variance, the temporal-reversal premise and Lemma 1's propagation mechanism fail.

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Extended reading notes

Core claim

Restage4D's central claim: a monocular video can be reanimated with new synthetic motion while retaining its geometry by treating the reconstruction as a puppet with shared motion coefficients. The method plays the base video backward, splices it to a synthetic driving video, and optimizes both through one low-rank set of SE(3) motion bases; Lemma 1 states that with temporal smoothness this joint training gives $\mathrm{Var}_{t\in[0,t_1]}(d(t)) < \sigma_0$ for pairwise distances, so real-clip supervision stabilizes the edited segment. Occlusion-aware rigidity and disocclusion backtracing protect invisible and newly revealed regions. DAVIS and PointOdyssey experiments show improved consistenc

Load-bearing premise

The rewind scheme rests on the unproved premise that deformable 3D reconstruction from a video played backward gives the same geometry as forward playback, so that the reversed real clip and the synthetic clip can be trained as one shared articulation; if that invariance fails, joint training may fit two incompatible deformation fields instead of propagating supervision.

Editorial extensions

If this is right

  • Real-world video becomes an articulation prior that can supervise many generated motions from the same scene, not just one reconstruction.
  • Artifacts in diffusion-generated driving videos—limb swaps, fused background, geometry changes after occlusion—can be corrected during reconstruction instead of requiring a separate filtering step.
  • Occluded and disoccluded regions stay geometrically consistent under novel motion, extending usable 4D restaging to long or complex motions.
  • The variance-reduction lemma gives a quantitative target: joint training with a supervised real clip should make pairwise 3D distances in the edited clip more stable, which is checkable independently of rendering quality.
  • A restaged 4D asset is produced by truncating the jointly reconstructed scene to the edited clip, preserving original appearance with new dynamics.

Reading between the lines

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

  • The paper does not test its temporal-reversal invariance premise; a direct forward-versus-rewound reconstruction comparison would confirm it, and a failure would mean joint training could fit two incompatible deformation fields.
  • The appendix proof (labeled Lemma 2 while the main text calls it Lemma 1) is qualitative: it argues smoothness suppresses fluctuation rather than deriving a bound; the ablation table is what substantiates the variance claim.
  • If the rewind scheme holds, one real video could supervise a batch of generated variants, and the reconstruction loss could double as a physical-consistency filter that flags generated clips outside the real articulation space.
  • The paper's conclusion notes that severe generated artifacts and highly deformable or textureless objects can exceed the method's assumptions, so the correction and propagation claims are bounded, not universal.
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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

3 major / 4 minor

Summary. The paper introduces 4D restaging: given a monocular base video and a text prompt, an image-to-video diffusion model generates a driving video, and the scene is reconstructed as a deformable 4D Gaussian representation with low-rank motion bases. The proposed Restage4D pipeline uses (i) a video-rewinding joint-training scheme that temporally concatenates the reversed base video with the driving video and optimizes a shared motion representation, (ii) an occlusion-aware rigidity loss, and (iii) a disocclusion backtracing mechanism. The authors claim this preserves geometry from the real video while reanimating the scene with novel synthetic motion, and can correct artifacts produced by video diffusion models. Evaluation is reported on DAVIS and PointOdyssey, including a 3D tracking benchmark with ground truth.

Significance. The problem is timely and relevant: using real video as a geometric supervisor for synthetic-motion 4D content creation is a sensible direction, and the PointOdyssey evaluation provides an external ground-truth benchmark. The paper also promises code release. If the central mechanism works, the task itself is a useful contribution. However, the formal justification for the core joint-training scheme is not established, and the quantitative evidence is currently too thin to support the strength of the claims. The significance is therefore conditional on additional rigorous proof or empirical validation.

major comments (3)
  1. [Section 3.2 / Appendix A] Lemma 1 is load-bearing but not proven. The appendix's 'Proof of Lemma 2' essentially asserts that temporal smoothness reduces variance; no rigorous derivation of Var_{t in [0,t1]}(d(t)) < sigma0 is given. The proof also conflates 'without supervision' with 'without regularization'. Since the central geometry-propagation claim rests on this lemma, it must either be proved rigorously with explicit assumptions and quantitative bounds, or be presented as an empirical design choice supported by measurements. As written, the lemma is not a proof.
  2. [Section 3.2, paragraph 2] The video-rewinding scheme relies on the unverified assumption that deformable 3D reconstruction is invariant to temporal direction. This is not a trivial property: the initialization uses TAPIR 2D tracks, camera pose estimation, and visibility/occlusion patterns, all of which are time-asymmetric, and the optimization can converge to different canonical configurations when the sequence is reversed. If the reversal changes the reconstruction, then the 'supervised' segment in Lemma 1 is not the original geometry, and the claimed propagation collapses. Please add a direct experiment reconstructing the base video forward and backward and comparing canonical Gaussians, rendered geometry, and motion coefficients; report quantitative discrepancies.
  3. [Table 1 and Section 4.2] The quantitative evaluation is not yet convincing. Table 1 reports only training-view PSNR and uses three metrics introduced in this paper (OCV CLIP, volume consistency with hand-set gamma=1.5, edge consistency); no validation of these metrics or significance tests are provided. Several deltas are within the reported standard deviations (e.g., Baseline 26.71±1.97 vs +Joint 26.58±2.21 for PSNR). Table 2 reports 3D tracking loss without the number of sequences, error bars, or variance, making it impossible to assess whether the differences are meaningful. Please report per-sequence paired confidence intervals/significance tests, specify the number of sequences, and justify or ablate the gamma choice.
minor comments (4)
  1. [Appendix A] The appendix heading says 'Proof of Lemma 2' while the main text states Lemma 1; the numbering should be consistent. Also, the proof is informal and should be clearly labeled as intuition if it cannot be made rigorous.
  2. [Section 4.1] The 'Baseline' method is not defined. It should be stated explicitly (presumably Shape-of-Motion without the proposed losses) and compared with at least one existing monocular deformable reconstruction method.
  3. [Equations (5) and (9)] Thresholds tau0/tau1 in Equation (5) are not specified in the hyperparameters; please provide values and sensitivity. In Equation (9), the direction of the metric is counterintuitive (higher is better despite a negative log); please clarify and justify gamma=1.5.
  4. [General] Typos and formatting: 'LRegidity_ref ine' in Equation (6), 'PointOdessy' in Table 2 caption, and 'Bootstap' in the references. Please also state how many sequences were used in Table 2 and whether they overlap with the 20 DAVIS sequences.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

Restage4D's central claim is that a video-rewinding joint-training scheme, an occlusion-aware rigidity loss, and disocclusion backtracing propagate geometry from a real base video into a synthetic driving video. This is a proposed training objective, not a quantity fitted to the metric it is later used to support. The formal statement (Lemma 1, with the appendix proof mislabeled as 'Proof of Lemma 2') is informal and under-specified: it asserts that adding smoothness/supervision reduces temporal variance, and then concludes the desired inequality. That is a proof gap or a non-rigorous step, but it is not circular in the structural sense—the inequality is not obtained by substituting a definition, renaming a fitted parameter, or importing a uniqueness theorem from the authors' own prior work. The temporal-reversal invariance premise ('We observe that the deformable 3D reconstruction frameworks are invariant to temporal direction, reconstructing from a video playing in a reversed temporal order would result in the same reconstruction') is an explicit unverified assumption; an unsupported premise is a correctness risk, not a circular derivation. The paper also has independent external grounding: the 3D tracking evaluation uses PointOdyssey ground-truth trajectories (Table 2), and the method builds on Shape-of-Motion and standard 2D tracking/depth priors by citation, without relying on self-citations as load-bearing evidence. The custom DAVIS metrics (CLIP-OCV, volume consistency, edge consistency) are consistent with the method's objectives, but they are computed from reconstructed outputs rather than being fitted parameters renamed as predictions; the hand-set gamma=1.5 is arbitrary but not circular. Overall, no load-bearing step reduces by construction to its own inputs.

Assumptions & free parameters 5 free parameters · 6 assumptions · 1 invented entities

The method rests on several hand-tuned quantities (rigidity weight, basis count, visibility thresholds, metric exponent), domain assumptions about temporal-reversal invariance and self-referential depth-based occlusion masks, and one new model component (backtraced Gaussians). The largest epistemic cost is the assumed temporal-reversal invariance of the underlying reconstruction, which is not justified.

free parameters (5)
  • Rigidity loss weight = 10^-3
    Chosen in Appendix B by scanning 0 to 10^-1 in Fig. 7 for the best rigidity/tracking trade-off; it controls the strength of the occlusion-aware rigidity loss that underpins the geometry-preservation claim.
  • Number of motion bases K = 100
    Increased from a typical low-rank setting to '50-100' and fixed at 100 based on the same trade-off analysis; the number of bases controls deformation expressiveness and no principled rule is given.
  • Smooth-step visibility thresholds tau0, tau1 = not reported
    Equation 5 defines occluded versus visible regions for the occlusion-aware rigidity loss; the values are described as 'pre-defined' but not given, so the occlusion mask is effectively tuned by hand.
  • Volume consistency exponent gamma = 1.5
    Equation 9; the exponent amplifies consistency scores in the self-authored metric used for the main ablation claim; no justification other than hand choice.
  • k-NN graph size for rigidity loss = not reported
    The local graph Omega in Eqs. 2 and 6 determines which point pairs are assumed rigid; the value is unspecified though it directly affects the loss.
assumptions (6)
  • domain assumption Temporal reversal invariance of the underlying deformable 3D reconstruction
    Section 3.2 (paragraph 2): the entire rewinding trick requires that playing the video backwards gives the same reconstruction; stated as an observation, no proof or citation provided.
  • ad hoc to paper Joint training with temporal smoothness reduces pairwise distance variance in the unsupervised segment
    Appendix A: the Lemma 1/2 claim is that smoothness reduces variance, but the proof restates that smoothness reduces fluctuation; the variance reduction is treated as a premise, not derived.
  • domain assumption The rendered depth buffer is accurate enough to define occluded regions during optimization
    Section 3.3, Eqs. 4-5: the invisibility score is computed from the depth of the Gaussians currently being optimized, which is self-referential during the photometric fusion stage.
  • domain assumption Upstream 2D priors (Tapir, MegaSAM, VideoDepthAnything, SAM, Track Anything) are correct enough for initialization and backtracing
    Appendix B.1 and Section 3.1: scene flow, correspondences, and depth all come from these models, and their errors are not modeled.
  • domain assumption CLIP similarity is a valid proxy for geometric consistency of occluded regions
    Section 4.1 OCV CLIP: used as a headline geometry metric without validation against geometric ground truth.
  • standard math Standard 3D Gaussian Splatting rendering model and SE(3) motion composition in Eq. 1 are taken as given
    Section 3.1: these are standard constructions from the cited literature; they are background, not the contribution.
invented entities (1)
  • Backtraced canonical Gaussians for disoccluded points independent evidence
    purpose: Section 3.4: insert new Gaussian splats into canonical space for points visible only in the driving video, so disoccluded geometry is represented in the shared scene.
    These are not free-standing postulates: their positions are computed by backtracing tracked points from the driving video through Eq. 8, so they are tied to observed pixels. However, they are new model components added to patch the canonical reconstruction and could conflict with the original geometry if tracking is wrong.

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Pith. "Pith review of Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video." pith.science (2026). https://pith.science/paper/W4L2PP3F

@misc{pith2026250806715,
  author       = {Pith},
  title        = {Pith review of: Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W4L2PP3F}},
  note         = {Machine review of arXiv:2508.06715}
}
read the original abstract

Creating deformable 3D content has gained increasing attention with the rise of text-to-image and image-to-video generative models. While these models provide rich semantic priors for appearance, they struggle to capture the physical realism and motion dynamics needed for authentic 4D scene synthesis. In contrast, real-world videos can provide physically grounded geometry and articulation cues that are difficult to hallucinate. One question is raised: \textit{Can we generate physically consistent 4D content by leveraging the motion priors of the real-world video}? In this work, we explore the task of reanimating deformable 3D scenes from a single video, using the original sequence as a supervisory signal to correct artifacts from synthetic motion. We introduce \textbf{Restage4D}, a geometry-preserving pipeline for video-conditioned 4D restaging. Our approach uses a video-rewinding training strategy to temporally bridge a real base video and a synthetic driving video via a shared motion representation. We further incorporate an occlusion-aware rigidity loss and a disocclusion backtracing mechanism to improve structural and geometry consistency under challenging motion. We validate Restage4D on DAVIS and PointOdyssey, demonstrating improved geometry consistency, motion quality, and 3D tracking performance. Our method not only preserves deformable structure under novel motion, but also automatically corrects errors introduced by generative models, revealing the potential of video prior in 4D restaging task. Source code and trained models will be released.

Figures

Figures reproduced from arXiv: 2508.06715 by the authors.

Figure 1
Figure 1. We show the input and output for the Restage4D in the left. Given a base video and a [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Pipeline of Restage4D. To perform a 4D restaging task, a base video and a text prompt [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Samples of 4D motion creation. For the same input base video, we can create different [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Samples of geometry preserving for occlusion view. For each restaged 4D scene, we [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Visualization on PointOdessy dataset [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Samples of geometry and motion correction. Conditioning on the base video, Restage4D [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Effect of changing the number of bases. Let us define: f(t) := X K k=1 βk · ∆k(t), then d 2 (t) = ∥f(t)∥ 2 . The temporal variance of d(t) satisfies: Vart∈[0,t1](d(t)) = Vart∈[0,t1](∥f(t)∥). Since f(t) is a fixed linear combination of smoother functions {∆k(t)}, its te…
Figure 8
Figure 8. Figure 8: Examples of motion prompts and corresponding base/drive videos. Each column shows [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Some samples of 4D restaging on in-the-wild video, which are collected from Internet. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]

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Reference graph

Works this paper leans on

53 extracted references · 42 canonical work pages

  1. [1]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10 684–10 695. 1, 3

    work page 2022

  2. [2]

    Generative adversarial nets,

    I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial nets,”Advances in neural information processing systems, vol. 27, 2014. 1

    work page 2014

  3. [3]

    Make-a-video: Text-to-video generation without text-video data,

    U. Singer, A. Polyak, T. Hayes, X. Yin, J. An, S. Zhang, Q. Hu, H. Yang, O. Ashual, O. Gafni et al., “Make-a-video: Text-to-video generation without text-video data,” arXiv preprint arXiv:2209.14792, 2022. 1

    arXiv 2022

  4. [4]

    Instant-3d: Instant neural radiance field training towards on-device ar/vr 3d reconstruction,

    S. Li, C. Li, W. Zhu, B. Yu, Y . Zhao, C. Wan, H. You, H. Shi, and Y . Lin, “Instant-3d: Instant neural radiance field training towards on-device ar/vr 3d reconstruction,” in Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023, pp. 1–13. 1

    work page 2023

  5. [5]

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

    B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,”arXiv preprint arXiv:2209.14988, 2022. 1

    arXiv 2022

  6. [6]

    Text-to-4d dynamic scene generation,

    U. Singer, S. Sheynin, A. Polyak, O. Ashual, I. Makarov, F. Kokkinos, N. Goyal, A. Vedaldi, D. Parikh, J. Johnson et al., “Text-to-4d dynamic scene generation,”arXiv preprint arXiv:2301.11280, 2023. 1

    arXiv 2023

  7. [7]

    Animate124: Animating one image to 4d dynamic scene,

    Y . Zhao, Z. Yan, E. Xie, L. Hong, Z. Li, and G. H. Lee, “Animate124: Animating one image to 4d dynamic scene,” arXiv preprint arXiv:2311.14603, 2023. 1

    arXiv 2023

  8. [8]

    4d-fy: Text-to-4d generation using hybrid score distillation sampling,

    S. Bahmani, I. Skorokhodov, V . Rong, G. Wetzstein, L. Guibas, P. Wonka, S. Tulyakov, J. J. Park, A. Tagliasacchi, and D. B. Lindell, “4d-fy: Text-to-4d generation using hybrid score distillation sampling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 7996–8006. 1, 3

    work page 2024

Show all 53 references
  1. [9]

    Shape of motion: 4d reconstruction from a single video,

    Q. Wang, V . Ye, H. Gao, J. Austin, Z. Li, and A. Kanazawa, “Shape of motion: 4d reconstruction from a single video,” arXiv preprint arXiv:2407.13764, 2024. 2, 3, 4, 5

    2024

  2. [10]

    Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds,

    J. Lei, Y . Weng, A. Harley, L. Guibas, and K. Daniilidis, “Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds,” arXiv preprint arXiv:2405.17421, 2024. 2, 3

    2024 arXiv

  3. [11]

    Articulated mesh animation from multi-view silhouettes,

    D. Vlasic, I. Baran, W. Matusik, and J. Popovi´c, “Articulated mesh animation from multi-view silhouettes,” in Acm Siggraph 2008 papers, 2008, pp. 1–9. 2

    2008

  4. [12]

    A benchmark dataset and evaluation methodology for video object segmentation,

    F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 724–732. 2, 6

    2016

  5. [13]

    Pointodyssey: A large-scale synthetic dataset for long-term point tracking,

    Y . Zheng, A. W. Harley, B. Shen, G. Wetzstein, and L. J. Guibas, “Pointodyssey: A large-scale synthetic dataset for long-term point tracking,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 19 855–19 865. 2, 6

    2023

  6. [14]

    Nerf: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021. 3

    2021

  7. [15]

    Tensorf: Tensorial radiance fields,

    A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance fields,” inEuropean conference on computer vision. Springer, 2022, pp. 333–350

    2022

  8. [16]

    Instant neural graphics primitives with a multiresolution hash encoding,

    T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM transactions on graphics (TOG), vol. 41, no. 4, pp. 1–15, 2022

    2022

  9. [17]

    Nerf in the wild: Neural radiance fields for unconstrained photo collections,

    R. Martin-Brualla, N. Radwan, M. S. Sajjadi, J. T. Barron, A. Dosovitskiy, and D. Duckworth, “Nerf in the wild: Neural radiance fields for unconstrained photo collections,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 7210–7219

    2021

  10. [18]

    Plenoxels: Radiance fields without neural networks,

    S. Fridovich-Keil, A. Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 5501–5510

    2022

  11. [19]

    Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,

    J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 5855–5864. 10

    2021

  12. [20]

    K-planes: Explicit radiance fields in space, time, and appearance,

    S. Fridovich-Keil, G. Meanti, F. R. Warburg, B. Recht, and A. Kanazawa, “K-planes: Explicit radiance fields in space, time, and appearance,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 12 479–12 488. 3

    2023

  13. [21]

    3d gaussian splatting for real-time radiance field rendering

    B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering.” ACM Trans. Graph., vol. 42, no. 4, pp. 139–1, 2023. 3

    2023

  14. [22]

    D-nerf: Neural radiance fields for dynamic scenes,

    A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-nerf: Neural radiance fields for dynamic scenes,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 318–10 327. 3

    2021

  15. [23]

    Nerfies: Deformable neural radiance fields,

    K. Park, U. Sinha, J. T. Barron, S. Bouaziz, D. B. Goldman, S. M. Seitz, and R. Martin-Brualla, “Nerfies: Deformable neural radiance fields,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 5865–5874. 3

    2021

  16. [24]

    Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields,

    K. Park, U. Sinha, P. Hedman, J. T. Barron, S. Bouaziz, D. B. Goldman, R. Martin-Brualla, and S. M. Seitz, “Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields,” arXiv preprint arXiv:2106.13228, 2021. 3

    2021 arXiv

  17. [25]

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

    Z. Yang, X. Gao, W. Zhou, S. Jiao, Y . Zhang, and X. Jin, “Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 20 331–20 341. 3

    2024

  18. [26]

    3d geometry-aware deformable gaussian splatting for dynamic view synthesis,

    Z. Lu, X. Guo, L. Hui, T. Chen, M. Yang, X. Tang, F. Zhu, and Y . Dai, “3d geometry-aware deformable gaussian splatting for dynamic view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 8900–8910

    2024

  19. [27]

    4d gaussian splatting for real-time dynamic scene rendering,

    G. Wu, T. Yi, J. Fang, L. Xie, X. Zhang, W. Wei, W. Liu, Q. Tian, and X. Wang, “4d gaussian splatting for real-time dynamic scene rendering,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 20 310–20 320

    2024

  20. [28]

    Deformable 3d gaussian splatting for animatable human avatars,

    H. Jung, N. Brasch, J. Song, E. Perez-Pellitero, Y . Zhou, Z. Li, N. Navab, and B. Busam, “Deformable 3d gaussian splatting for animatable human avatars,” arXiv preprint arXiv:2312.15059, 2023. 3

    2023 arXiv

  21. [29]

    Depth anything: Unleashing the power of large-scale unlabeled data,

    L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, “Depth anything: Unleashing the power of large-scale unlabeled data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 10 371–10 381. 3

    2024

  22. [30]

    Tapir: Tracking any point with per-frame initialization and temporal refinement,

    C. Doersch, Y . Yang, M. Vecerik, D. Gokay, A. Gupta, Y . Aytar, J. Carreira, and A. Zisserman, “Tapir: Tracking any point with per-frame initialization and temporal refinement,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 10 061–10 072. 3, 6

    2023

  23. [31]

    Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,

    Z. Teed and J. Deng, “Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,” Advances in neural information processing systems, vol. 34, pp. 16 558–16 569, 2021. 3

    2021

  24. [32]

    Articulated body motion capture by annealed particle filtering,

    J. Deutscher, A. Blake, and I. Reid, “Articulated body motion capture by annealed particle filtering,” in Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), vol. 2. IEEE, 2000, pp. 126–133. 3

    2000

  25. [33]

    Tour into the picture: using a spidery mesh interface to make animation from a single image,

    Y . Horry, K.-I. Anjyo, and K. Arai, “Tour into the picture: using a spidery mesh interface to make animation from a single image,” in Proceedings of the 24th annual conference on Computer graphics and interactive techniques, 1997, pp. 225–232. 3

    1997

  26. [34]

    Farhat, M

    C. Farhat, M. Lesoinne, and P. Le Tallec, “Load and motion transfer algorithms for fluid/structure inter- action problems with non-matching discrete interfaces: Momentum and energy conservation, optimal discretization and application to aeroelasticity,” Computer methods in app...

    1998

  27. [35]

    Retargetting motion to new characters,

    M. Gleicher, “Retargetting motion to new characters,” inProceedings of the 25th annual conference on Computer graphics and interactive techniques, 1998, pp. 33–42. 3

    1998

  28. [36]

    Interactive control of avatars animated with human motion data,

    J. Lee, J. Chai, P. S. Reitsma, J. K. Hodgins, and N. S. Pollard, “Interactive control of avatars animated with human motion data,” in Proceedings of the 29th annual conference on Computer graphics and interactive techniques, 2002, pp. 491–500. 3

    2002

  29. [37]

    Deformation transfer for triangle meshes,

    R. W. Sumner and J. Popovi´c, “Deformation transfer for triangle meshes,” ACM Transactions on graphics (TOG), vol. 23, no. 3, pp. 399–405, 2004. 3 11

    2004

  30. [38]

    Skeleton-aware networks for deep motion retargeting,

    K. Aberman, P. Li, D. Lischinski, O. Sorkine-Hornung, D. Cohen-Or, and B. Chen, “Skeleton-aware networks for deep motion retargeting,”ACM Transactions on Graphics (TOG), vol. 39, no. 4, pp. 62–1,

  31. [39]

    A modular neural motion retargeting system decoupling skeleton and shape perception,

    J. Zhang, Z. Tu, J. Weng, J. Yuan, and B. Du, “A modular neural motion retargeting system decoupling skeleton and shape perception,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 3

    2024

  32. [40]

    Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes,

    Y .-H. Huang, Y .-T. Sun, Z. Yang, X. Lyu, Y .-P. Cao, and X. Qi, “Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 4220–4230. 3

    2024

  33. [41]

    D-miso: Editing dynamic 3d scenes using multi-gaussians soup,

    J. Waczynska, P. Borycki, J. Kaleta, S. Tadeja, and P. Spurek, “D-miso: Editing dynamic 3d scenes using multi-gaussians soup,” Advances in Neural Information Processing Systems, vol. 37, pp. 107 865–107 889,

  34. [42]

    B. F. Labs, “Flux,” https://github.com/black-forest-labs/flux, 2024. 3

    2024

  35. [43]

    Video generation models as world simulators,

    T. Brooks, B. Peebles, C. Holmes, W. DePue, and Y . Guo, “Video generation models as world simulators,” https://openai.com/index/video-generation-models-as-world-simulators/, 2024, openAI Technical Report. 3, 5

    2024

  36. [44]

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

    R. Liu, R. Wu, B. Van Hoorick, P. Tokmakov, S. Zakharov, and C. V ondrick, “Zero-1-to-3: Zero-shot one image to 3d object,” in Proceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 9298–9309. 3

    2023

  37. [45]

    Diffusion4d: Fast spatial-temporal consistent 4d generation via video diffusion models,

    H. Liang, Y . Yin, D. Xu, H. Liang, Z. Wang, K. N. Plataniotis, Y . Zhao, and Y . Wei, “Diffusion4d: Fast spatial-temporal consistent 4d generation via video diffusion models,” arXiv preprint arXiv:2405.16645,

    arXiv

  38. [46]

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

    J. Tang, J. Ren, H. Zhou, Z. Liu, and G. Zeng, “Dreamgaussian: Generative gaussian splatting for efficient 3d content creation,” arXiv preprint arXiv:2309.16653, 2023. 3

    2023 arXiv

  39. [47]

    Instruct-nerf2nerf: Editing 3d scenes with instructions,

    A. Haque, M. Tancik, A. A. Efros, A. Holynski, and A. Kanazawa, “Instruct-nerf2nerf: Editing 3d scenes with instructions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 19 740–19 750. 3

    2023

  40. [48]

    Dreambooth3d: Subject-driven text-to-3d generation,

    A. Raj, S. Kaza, B. Poole, M. Niemeyer, N. Ruiz, B. Mildenhall, S. Zada, K. Aberman, M. Rubinstein, J. Barron et al., “Dreambooth3d: Subject-driven text-to-3d generation,” in Proceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 2349–2359. 3

    2023

  41. [49]

    As-rigid-as-possible shape manipulation,

    T. Igarashi, T. Moscovich, and J. F. Hughes, “As-rigid-as-possible shape manipulation,”ACM transactions on Graphics (TOG), vol. 24, no. 3, pp. 1134–1141, 2005. 5

    2005

  42. [50]

    Bootstap: Bootstrapped training for tracking-any-point,

    C. Doersch, P. Luc, Y . Yang, D. Gokay, S. Koppula, A. Gupta, J. Heyward, I. Rocco, R. Goroshin, J. Carreira et al., “Bootstap: Bootstrapped training for tracking-any-point,” in Proceedings of the Asian Conference on Computer Vision, 2024, pp. 3257–3274. 6

    2024

  43. [51]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in International conference on machine learning. PmLR, 2021, pp. 8748–8763. 7

    2021

  44. [52]

    Towards a multiscale point cloud structural similarity metric,

    D. Lazzarotto and T. Ebrahimi, “Towards a multiscale point cloud structural similarity metric,” in2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 2023, pp. 1–6. 7

    2023

  45. [53]

    Inferring point cloud quality via graph similarity,

    Q. Yang, Z. Ma, Y . Xu, Z. Li, and J. Sun, “Inferring point cloud quality via graph similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 3015–3029, 2022. 7 A Proof of Lemma 2 We introduce Lemma 2 to demonstrate the necessity of using...

    2022

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.