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REVIEW 5 major objections 5 minor 3 cited by

PhysAnimator: Physics-Guided Generative Cartoon Animation

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read One anime still can become a physics-driven animation without hand-drawn in-betweens.

desk verdict Solid pipeline, unverified physics-guidance claim, and thin evaluation; worth reviewing but needs real revisions. read the letter →

arxiv 2501.16550 v2 pith:D5YXX2DZ submitted 2025-01-27 cs.GR cs.CV

Tianyi Xie , Yiwei Zhao , Ying Jiang , Chenfanfu Jiang This is my paper · ORCID
classification cs.GRcs.CV
keywords animeanimationphysics-baseddeformablebodysimulationvideodiffusionmodelssketch-guidedrenderingimage-to-videogenerationopticalflowcartooninterpolation
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 introduces PhysAnimator, a pipeline that turns a single anime illustration into a short animation whose motion is computed by a physics simulation rather than invented by a generative model. It segments the object of interest, builds a 2D triangular mesh, and runs an image-space deformable-body simulation; the simulated deformation is converted into optical flow, which warps an extracted sketch into a texture-agnostic motion guide, and a sketch-conditioned video diffusion model renders the final frames. Users can steer the motion with energy strokes that push flow particles through the mesh and with rigging points that anchor or wave specific regions. The authors claim this combination beats current image-to-video and trajectory-control methods on automatic quality metrics and in pairwise user preference while preserving the look of the original illustration. If true, it would let animators and hobbyists produce consistent, physics-based anime motion from a single still without drawing in-betweens.

What carries the argument

The load-bearing mechanism is an image-space deformable-body simulator on a 2D conforming-Delaunay triangulation of the segmented anime object. The Fixed Corotated energy density resists stretching and volume change and returns the mesh toward its rest shape, external forces come from user-drawn energy strokes and rigging points, and semi-implicit Euler integration advances the mesh; each triangle's deformation map then defines a pixel displacement, producing the optical flow sequence. The other half of the mechanism is the texture-agnostic sketch: the extracted sketch is forward-warped by the flow with weights proportional to displacement magnitude, and a video diffusion model with a sketch-conditioned control module renders the final frames. The sketch is what lets the simulation's precise motion reach a generative renderer without forcing the renderer to reproduce warped textures or fill occlusion holes itself.

What would settle it

Run PhysAnimator on a set of anime stills and measure the optical flow of the output videos against the simulated flow sequence that warped the sketches; large or growing mean endpoint error would show the diffusion renderer overriding the physics. A second check is an ablation that feeds the same sketch-conditioned diffusion model sketches warped by smooth random flows; if viewers cannot distinguish those videos from the simulated ones, the simulation is not carrying the perceived motion.

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Extended reading notes

Core claim

The central claim is that physics-based deformable-body simulation can serve as the motion backbone for generative anime animation. The simulated deformation map is converted into pixelwise optical flow, the flow warps a sketch instead of the textured image, and a video diffusion model conditioned on the warped sketch fills in shading, texture, and occluded regions. Because the sketch is texture-agnostic, small segmentation errors and boundary artifacts are smoothed away before generation, and because the control signal comes from simulation, the motion keeps a geometric consistency that purely data-driven flow predictors lack. The paper also claims that simulated motion alone looks too rigid by anime standards, so an optional data-driven cartoon interpolation stage regenerates in-between frames from selected keyframes and adds expressive, deliberately non-physical dynamics; in the paper's user study, 70.4% of participants preferred this enhanced version. Experiments on a twenty-image anime test set report the method leading on most automatic video-quality metrics and winning pairwise user preference against four image-to-video baselines.

Load-bearing premise

The 'physics-guided' claim rests on the assumption that the sketch-guided video diffusion model follows the warped sketch closely enough for the simulated motion to survive in the final video, yet the paper never quantitatively compares simulated flow with the flow of the generated frames.

Editorial extensions

If this is right

  • A single illustration plus a few energy strokes can produce wind-driven hair, clothing, and plant motion without layered image files or hand-drawn in-betweens.
  • Because the control signal is simulated optical flow, the generated motion stays geometrically consistent and avoids the whole-image shifts that trajectory-control baselines sometimes produce.
  • Warping a sketch instead of the textured image makes the pipeline tolerant to imperfect segmentation, since the diffusion renderer can refine a blurred or incomplete sketch.
  • The optional keyframe-interpolation stage lets artists trade a small drop in automatic fidelity scores for more exaggerated, anime-like motion, which users preferred 70.4% of the time in the paper's study.
  • Material stiffness and force settings give animators continuous control between rigid and soft behavior, and simulated collisions allow multi-object scenes to respond to each other.

Reading between the lines

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

  • A natural falsification check the paper leaves open: compare the optical flow of generated videos with the simulated flow; if the diffusion renderer often overrides the sketch, the physics guides the pipeline but not reliably the output.
  • The texture-agnostic sketch control should transfer to other flat-art styles such as comics, watercolor, or line art whenever a sketch extractor exists, so the method is not inherently limited to anime.
  • Because the simulation is planar image-space, it gives stylized 2D deformation rather than true cloth or hair physics; adding layered depth or a 3D proxy would likely improve occlusion and collision behavior.
  • A testable extension is to measure how much user effort, in strokes and rigging points, is needed to achieve a target motion and whether adjusted stiffness parameters produce perceptibly different motion styles for non-expert viewers.
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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

5 major / 5 minor

Summary. The paper proposes PhysAnimator, a framework that generates anime-style animation from a single illustration by combining image-space deformable-body physics simulation with a sketch-guided video diffusion model. The pipeline segments a target object, builds a 2D triangular mesh, simulates dynamics with a Fixed Corotated constitutive model, extracts per-pixel optical flow, warps an extracted sketch, and renders frames with a ControlNet-conditioned latent video diffusion model. An optional ToonCrafter-based interpolation stage adds extra stylized dynamics. The paper claims to outperform existing image-to-video and motion-control baselines, based on VideoScore metrics, FID, qualitative comparisons, and a user study.

Significance. If the central claim is validated, the framework would be a practically useful authoring tool for cartoon animation, combining the controllability of physics simulation with the appearance synthesis of diffusion models. The physics component is standard and correctly formulated, and the approach is not circular: the simulation is independent of the learned generative models. The paper also offers user controls (energy strokes and rigging points) and presents qualitative demos. However, the current evaluation does not directly demonstrate that the simulated motion survives the generative rendering pipeline, and several quantitative claims are not supported by the reported numbers. The contribution is potentially significant, but the evidence in this version is insufficient.

major comments (5)
  1. [§3.3, Eq. (8), and §4 'Complementary Dynamics'] The central 'physics-guided' claim requires that the motion in the final video is the motion produced by the deformable-body simulation. The paper never quantitatively compares the simulated optical flow F0→t with the optical flow of the generated video. Instead, the sketch input is Gaussian-blurred and the ControlNet is allowed to 'refine' the sketches, and the optional ToonCrafter stage explicitly adds 'non-physical' dynamics with a control scale of only 0.1. I ask for a direct flow-to-flow evaluation (e.g., compute optical flow of the generated frames with RAFT and report EPE or warping error against F0→t) and an ablation measuring flow fidelity with and without the sketch ControlNet and with and without ToonCrafter. Without such evidence, the physical plausibility of the output is an assumption rather than a demonstrated property.
  2. [§4.1, Table 1 and §5] The claim that the method 'outperforms existing video diffusion methods' is not supported by the reported numbers. The FID of PhysAnimator is 90.4, substantially worse than Cinemo's 49.5, and the dynamic-degree score (2.48) is lower than DragAnything (2.97), DynamiCrafter (2.53), and Motion-I2V (2.66). The explanations that Cinemo produces static videos and that other methods' high dynamics come from distortion are plausible but are not quantified. Please report metrics that directly measure physics fidelity or motion consistency, and provide significance tests or confidence intervals over the 200 videos per method. The wording 'outperforms' should be softened unless supported by additional evidence.
  3. [§4.1, Table 2 and Appendix E] The user study is reported only as preference percentages, with no participant count, no number of trials per participant, and no significance tests or confidence intervals. The ablation study in Appendix E reports a 70.4% preference for the dynamics-enhancement module without any statistical grounding. Without these details, the 'consistently outperforms' conclusion from the user study is not statistically supported. Please provide the full protocol, participant numbers, and appropriate significance testing.
  4. [§4, 'Baseline'] For the trajectory-based baselines Motion-I2V and DragAnything, the paper states that 'we extract trajectories from our animated results and use them as input for these methods.' This protocol makes the comparison depend on the authors' own pipeline: if the extracted trajectories are noisy, unrepresentative, or contain artifacts from the PhysAnimator rendering, the baselines are systematically disadvantaged. Please specify the trajectory extraction procedure, report what happens with user-specified trajectories, or otherwise justify that this protocol is fair to both categories of baselines.
  5. [Appendix C] The sentence 'We will carefully add these details to our revised appendix' indicates that training details are currently missing. The manuscript does not state the exact training steps, dataset splits, or precise integration of the LVCD and ToonCrafter ControlNets. This prevents reproducibility of the trained components. Please include the complete training and inference configuration in the final version.
minor comments (5)
  1. [§2.1 and §4.1] The model name 'Cinemo' is misspelled as 'Cinema' in two places; please correct these typos.
  2. [Appendix A, Eq. (12)] The phrase 'The internal force fir the fintpxq' should read 'The internal force fintpxq is derived as' or similar.
  3. [§3.1] The deformation gradient is denoted F, which later conflicts with the use of F0→t for optical flow fields. Consider renaming one of these symbols to avoid ambiguity.
  4. [Abstract] 'physically plausible meanwhile anime-stylized' is grammatically awkward; consider 'physically plausible yet anime-stylized'.
  5. [References] Reference [44] is listed as 'Physics-Based Simulation' without a complete author list or year; please provide the full citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the physics simulation, sketch warping, and diffusion rendering form an open pipeline whose output is not definitionally tied to its inputs.

full rationale

The derivation chain is modular and not self-referential. The optical flow F0→t is obtained by solving Eq. (6) with the Fixed Corotated energy of Eq. (7) on a 2D mesh, with user energy strokes and rigging as external inputs; none of these parameters are fitted to the test set or to the final video. The sketch sequence in Eq. (8) is a forward warp of S0 by the simulated flow, which is an independent transformation, and the sketch-guided ControlNet is trained on an external Sakuga-42M subset rather than on the test images. The optional ToonCrafter stage explicitly adds non-physical dynamics at control scale 0.1, which weakens the 'physics-guided' claim empirically but is not a circularity: the final video is not defined as the simulation output, nor is the simulation defined in terms of the final video. Self-citations such as VideoPhy, PhysGaussian, and PhysMotion appear as related-work references and are not load-bearing for the derivation. The evaluation protocol that feeds trajectories extracted from the authors' own results into Motion-I2V and DragAnything is a fairness concern, but it does not make the method's output equivalent to its inputs by construction. Missing verification of flow preservation through diffusion is a correctness or evidence gap, not a circularity. Appendix C's deferred training details are an incompleteness, not a circular step.

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

The central claim rests on standard physics plus a chain of domain assumptions about the 2D mesh model, the sketch representation, and the diffusion model's fidelity. Several hyperparameters (timestep, keyframe gap, control scale, data filters) are hand-chosen and affect the output. No new physical entities are introduced.

free parameters (4)
  • Simulation timestep dt = 0.001
    Set by hand in Appendix A; controls numerical stability and motion magnitude.
  • Keyframe gap n (ToonCrafter) = 15
    Chosen in Section 4 for the complementary dynamics module; affects how much non-physical motion is added.
  • ControlNet scale for ToonCrafter = 0.1
    Set in Section 4; controls how strictly the sketch guides the interpolation model.
  • Training data dynamics-score range = 0.05 to 0.7
    Filter threshold on Sakuga-42M in Section 4; arbitrary and affects the distribution of training dynamics.
assumptions (4)
  • standard math Standard continuum mechanics and FEM discretization (Newton's law, deformation gradient, Fixed Corotated energy)
    Used in Section 3.2 and Appendix A to compute dynamics; standard in graphics.
  • domain assumption The object of interest can be represented as a 2D triangulated deformable body in image space
    The method assumes anime cloth and hair can be modeled as a flat 2D elastic sheet, ignoring 3D structure and interpenetration; Section 3.2.
  • domain assumption The extracted sketch is a faithful texture-agnostic representation that, when warped, can be re-rendered by the diffusion model
    Section 3.3; if the sketch loses geometric or semantic detail, the rendered video may diverge from the reference and the simulated motion.
  • domain assumption The sketch-guided video diffusion model generalizes to unseen anime illustrations and preserves the control signal
    Sections 3.3 and 4; the paper provides no quantitative verification of control-signal fidelity.

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Cite this review

Pith. "Pith review of PhysAnimator: Physics-Guided Generative Cartoon Animation." pith.science (2026). https://pith.science/paper/D5YXX2DZ

@misc{pith2026250116550,
  author       = {Pith},
  title        = {Pith review of: PhysAnimator: Physics-Guided Generative Cartoon Animation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5YXX2DZ}},
  note         = {Machine review of arXiv:2501.16550}
}
read the original abstract

Creating hand-drawn animation sequences is labor-intensive and demands professional expertise. We introduce PhysAnimator, a novel approach for generating physically plausible meanwhile anime-stylized animation from static anime illustrations. Our method seamlessly integrates physics-based simulations with data-driven generative models to produce dynamic and visually compelling animations. To capture the fluidity and exaggeration characteristic of anime, we perform image-space deformable body simulations on extracted mesh geometries. We enhance artistic control by introducing customizable energy strokes and incorporating rigging point support, enabling the creation of tailored animation effects such as wind interactions. Finally, we extract and warp sketches from the simulation sequence, generating a texture-agnostic representation, and employ a sketch-guided video diffusion model to synthesize high-quality animation frames. The resulting animations exhibit temporal consistency and visual plausibility, demonstrating the effectiveness of our method in creating dynamic anime-style animations. See our project page for more demos: https://xpandora.github.io/PhysAnimator/

Figures

Figures reproduced from arXiv: 2501.16550 by the authors.

Figure 1
Figure 1. PhysAnimator is a novel framework that combines physics principles with video diffusion models to generate high-quality animations from static anime illustrations, allowing users to specify external forces or rigging points for custom effects. Abstract Creating hand-drawn animation sequences is labor￾intensive and demands professional expertise. We intro￾duce PhysAnimator, a novel approach for generating phys￾ically… view at source ↗
Figure 2
Figure 2. Method Overview. We begin by segmenting the object and creating a triangulated deformable mesh. Physics-based simulations are then used to generate dynamic optical flow fields, with users given the option to guide the motion through customizable energy strokes(shown as orange arrows) and rigging points(shown as red dots). The extracted sketch is warped using the computed optical flow and refined with a sketch-guided… view at source ↗
Figure 3
Figure 3. Qualitative Comparison. We compare our results against Cinemo [51], Drag Anything [74], DynamiCrafter [79] and Motion￾I2V [61]. Text prompts for Cinemo and DynamiCrafter are generated using ChatGPT-4V, while trajectories for Drag Anything and Motion￾I2V are extracted from our animated results. demonstrating the realistic motion dynamics enabled by our physics-based modeling. In addition, following [15, 38], we condu… view at source ↗
Figures from the paper (8 more)
Figure 5
Figure 5. Figure 5: Complementary Dynamics Enhancement. While physics-based animation maintains geometric consistency, it may lack the fluidity and exaggeration commonly seen in anime. We employ a data-driven interpolation module to enhance the motion dynamics, creating more natural-looki…
Figure 4
Figure 4. Figure 4: Sketch-Guided Rendering. Applying warping and in￾painting introduces artifacts due to segmentation inaccuracy. Soft￾inpainting [43] reduces these issues but can alter the content. Our sketch-guided rendering method produces high-quality results while preserving image d…
Figure 6
Figure 6. Figure 6: Flow Particles. Wind particles can simulate wind ef￾fects, creating natural swaying motions; repel particles act as col￾lision barriers, adjusting object trajectories; attract particles can pull objects toward specific points as desired. where x n, v n and x n`1 , v n`…
Figure 7
Figure 7. Figure 7: Additional Qualitative Comparison. We provide additional comparison results against Cinemo [51], Drag Anything (DA) [74], DynamiCrafter (DC) [79] and Motion-I2V [61]. C. Training Details We adopt the standard ControlNet [84] design, integrat￾ing each frame control sign…
Figure 8
Figure 8. Figure 8: Additional Qualitative Comparison. We provide additional comparison results against Cinemo [51], Drag Anything (DA) [74], DynamiCrafter (DC) [79] and Motion-I2V [61]. these details to our revised appendix. D. More Results D.1. Controllable Generation Our physics-based …
Figure 9
Figure 9. Figure 9: Additional Qualitative Comparison. We provide additional comparison results against Cinemo [51], Drag Anything (DA) [74], DynamiCrafter (DC) [79] and Motion-I2V [61] [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 11
Figure 11. Figure 11: Controllable Generation with Energy Stroke. By applying energy strokes with varying directions and strengths, our method enables the convenient generation of diverse dynamic ef￾fects [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: Multi-Object Interaction. Collisions are highlighted with red circles. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

89 extracted references · 44 canonical work pages · cited by 3 Pith papers

  1. [65]

    Physmotion: Physics- grounded dynamics from a single image

    Xiyang Tan, Ying Jiang, Xuan Li, Zeshun Zong, Tianyi Xie, Yin Yang, and Chenfanfu Jiang. Physmotion: Physics- grounded dynamics from a single image. arXiv preprint arXiv:2411.17189, 2024. 2

    arXiv 2024

  2. [1]

    Videophy: Evaluating phys- ical commonsense for video generation

    Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai- Wei Chang, and Aditya Grover. Videophy: Evaluating phys- ical commonsense for video generation. arXiv:2406.03520,

    arXiv

  3. [2]

    De- formable object animation using reduced optimal control

    Jernej Barbi ˇc, Marco da Silva, and Jovan Popovi ´c. De- formable object animation using reduced optimal control. In ACM SIGGRAPH 2009 papers, pages 1–9. 2009. 3

    2009

  4. [3]

    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:2311.15127, 2023. 2

    arXiv 2023

  5. [4]

    Align your latents: High-resolution video synthesis with la- tent diffusion models

    Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dock- horn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with la- tent diffusion models. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 22563–22575, 2023. 2

    2023

  6. [5]

    Stable- video: Text-driven consistency-aware diffusion video edit- ing

    Wenhao Chai, Xun Guo, Gaoang Wang, and Yan Lu. Stable- video: Text-driven consistency-aware diffusion video edit- ing. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision, pages 23040–23050, 2023. 6

    2023

  7. [6]

    Learning to generate line drawings that convey geometry and semantics

    Caroline Chan, Fredo Durand, and Phillip Isola. Learning to generate line drawings that convey geometry and semantics

  8. [7]

    Control-a-video: Controllable text-to-video generation with diffusion models

    Weifeng Chen, Yatai Ji, Jie Wu, Hefeng Wu, Pan Xie, Jiashi Li, Xin Xia, Xuefeng Xiao, and Liang Lin. Control-a-video: Controllable text-to-video generation with diffusion models. arXiv:2305.13840, 2023. 2

    arXiv 2023

Show all 89 references
  1. [8]

    Seine: Short-to-long video diffu- sion model for generative transition and prediction

    Xinyuan Chen, Yaohui Wang, Lingjun Zhang, Shaobin Zhuang, Xin Ma, Jiashuo Yu, Yali Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. Seine: Short-to-long video diffu- sion model for generative transition and prediction. In The Twelfth International Conference on Learning Representa- ti...

    2023

  2. [9]

    V3d: Video diffusion models are effective 3d generators

    Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, and Huaping Liu. V3d: Video diffusion models are effective 3d generators. arXiv:2403.06738, 2024. 2

    2024 arXiv

  3. [10]

    Deformable objects alive! ACM Transactions on Graphics (TOG), 31(4):1–9, 2012

    Stelian Coros, Sebastian Martin, Bernhard Thomaszewski, Christian Schumacher, Robert Sumner, and Markus Gross. Deformable objects alive! ACM Transactions on Graphics (TOG), 31(4):1–9, 2012. 3

    2012

  4. [11]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural informa- tion processing systems, 34:8780–8794, 2021. 2

    2021

  5. [12]

    Structure and content-guided video synthesis with diffusion models

    Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis. Structure and content-guided video synthesis with diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7346–7356, 2023. 2

    2023

  6. [13]

    Breath- ing life into sketches using text-to-video priors

    Rinon Gal, Yael Vinker, Yuval Alaluf, Amit Bermano, Daniel Cohen-Or, Ariel Shamir, and Gal Chechik. Breath- ing life into sketches using text-to-video priors. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4325–4336, 2024. 2

    2024

  7. [14]

    Preserve your own correlation: A noise prior for video diffusion models

    Songwei Ge, Seungjun Nah, Guilin Liu, Tyler Poon, Andrew Tao, Bryan Catanzaro, David Jacobs, Jia-Bin Huang, Ming- Yu Liu, and Yogesh Balaji. Preserve your own correlation: A noise prior for video diffusion models. In Proceedings of the IEEE/CVF International Conference on Comp...

    2023

  8. [15]

    Tokenflow: Consistent diffusion features for consistent video editing

    Michal Geyer, Omer Bar-Tal, Shai Bagon, and Tali Dekel. Tokenflow: Consistent diffusion features for consistent video editing. arxiv:2307.10373, 2023. 7

    2023 arXiv

  9. [16]

    Elemental Magic, Volume 2: The Technique of Special Effects Animation

    Joseph Gilland. Elemental Magic, Volume 2: The Technique of Special Effects Animation. Routledge, 2012. 3, 5

    2012

  10. [17]

    Videoswap: Customized video subject swapping with interactive semantic point cor- respondence

    Yuchao Gu, Yipin Zhou, Bichen Wu, Licheng Yu, Jia-Wei Liu, Rui Zhao, Jay Zhangjie Wu, David Junhao Zhang, Mike Zheng Shou, and Kevin Tang. Videoswap: Customized video subject swapping with interactive semantic point cor- respondence. In Proceedings of the IEEE/CVF Conference o...

    2024

  11. [18]

    Sparsectrl: Adding sparse controls to text-to-video diffusion models

    Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Sparsectrl: Adding sparse controls to text-to-video diffusion models. In European Conference on Computer Vision, pages 330–348. Springer, 2025. 2, 3

    2025

  12. [19]

    Vfusion3d: Learning scalable 3d generative models from video diffusion models

    Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning scalable 3d generative models from video diffusion models. In European Conference on Computer Vision, pages 333–350. Springer, 2025. 2

    2025

  13. [20]

    Videoscore: Building automatic metrics to simulate fine-grained human feedback for video genera- tion

    Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, et al. Videoscore: Building automatic metrics to simulate fine-grained human feedback for video genera- tion. arXiv:2406.15252, 2024. 2, 6, 8, 16, 17

    2024 arXiv

  14. [21]

    Latent video diffusion models for high-fidelity long video generation

    Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, and Qifeng Chen. Latent video diffusion models for high-fidelity long video generation. arXiv:2211.13221, 2022. 2

    2022 arXiv

  15. [22]

    Animate-a-story: Storytelling with retrieval-augmented video generation

    Yingqing He, Menghan Xia, Haoxin Chen, Xiaodong Cun, Yuan Gong, Jinbo Xing, Yong Zhang, Xintao Wang, Chao Weng, Ying Shan, et al. Animate-a-story: Storytelling with retrieval-augmented video generation. arXiv:2307.06940,

    arXiv

  16. [23]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems , 30, 2017. 6

    2017

  17. [24]

    Video dif- fusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video dif- fusion models. Advances in Neural Information Processing Systems, 35:8633–8646, 2022. 2

    2022

  18. [25]

    Lrm: Large reconstruction model for single image to 3d

    Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao 9 Tan. Lrm: Large reconstruction model for single image to 3d. arXiv:2311.04400, 2023. 4

    2023 arXiv

  19. [26]

    Taichi: a language for high-performance computation on spatially sparse data structures

    Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Fr ´edo Durand. Taichi: a language for high-performance computation on spatially sparse data structures. ACM Transactions on Graphics (TOG) , 38(6): 201, 2019. 6

    2019

  20. [27]

    Lvcd: Reference-based lineart video colorization with diffusion models

    Zhitong Huang, Mohan Zhang, and Jing Liao. Lvcd: Reference-based lineart video colorization with diffusion models. arXiv:2409.12960, 2024. 6, 14

    2024 arXiv

  21. [28]

    Video interpolation with diffu- sion models

    Siddhant Jain, Daniel Watson, Eric Tabellion, Ben Poole, Janne Kontkanen, et al. Video interpolation with diffu- sion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 7341– 7351, 2024. 2

    2024

  22. [29]

    Vmc: Video motion customization using temporal attention adap- tion for text-to-video diffusion models

    Hyeonho Jeong, Geon Yeong Park, and Jong Chul Ye. Vmc: Video motion customization using temporal attention adap- tion for text-to-video diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9212–9221, 2024. 2, 3

    2024

  23. [30]

    Handpainter-3d sketching in vr with hand-based physical proxy

    Ying Jiang, Congyi Zhang, Hongbo Fu, Alberto Cannav `o, Fabrizio Lamberti, Henry YK Lau, and Wenping Wang. Handpainter-3d sketching in vr with hand-based physical proxy. In Proceedings of the 2021 CHI conference on hu- man factors in computing systems, pages 1–13, 2021. 2

    2021

  24. [31]

    Region-aware color smudging

    Ying Jiang, Pengfei Xu, Congyi Zhang, Hongbo Fu, Henry Lau, and Wenping Wang. Region-aware color smudging. IEEE Transactions on Visualization and Computer Graph- ics, 2024. 2

    2024

  25. [32]

    Vr-gs: A physical dynamics-aware interactive gaussian splatting system in virtual reality

    Ying Jiang, Chang Yu, Tianyi Xie, Xuan Li, Yutao Feng, Huamin Wang, Minchen Li, Henry Lau, Feng Gao, Yin Yang, et al. Vr-gs: A physical dynamics-aware interactive gaussian splatting system in virtual reality. In ACM SIG- GRAPH 2024 Conference Papers, pages 1–1, 2024. 3

    2024

  26. [33]

    Dynamic sprites: artistic authoring of inter- active animations

    Ben Jones, Jovan Popovic, James McCann, Wilmot Li, and Adam Bargteil. Dynamic sprites: artistic authoring of inter- active animations. Computer Animation and Virtual Worlds, 26(2):97–108, 2015. 3

    2015

  27. [34]

    Example-based plastic deformation of rigid bodies

    Ben Jones, Nils Thuerey, Tamar Shinar, and Adam W Bargteil. Example-based plastic deformation of rigid bodies. ACM Transactions on Graphics (TOG), 35(4):1–11, 2016. 3

    2016

  28. [35]

    Motion amplifiers: sketching dy- namic illustrations using the principles of 2d animation

    Rubaiat Habib Kazi, Tovi Grossman, Nobuyuki Umetani, and George Fitzmaurice. Motion amplifiers: sketching dy- namic illustrations using the principles of 2d animation. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 4599–4609, 2016. 2, 3, 5

    2016

  29. [36]

    Auto-encoding variational bayes

    Diederik P Kingma. Auto-encoding variational bayes. arXiv:1312.6114, 2013. 3

    2013 arXiv

  30. [37]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 4015–4026, 2023. 5

    2023

  31. [38]

    Style transfer by relaxed optimal transport and self-similarity

    Nicholas Kolkin, Jason Salavon, and Gregory Shakhnarovich. Style transfer by relaxed optimal transport and self-similarity. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 10051–10060, 2019. 7

    2019

  32. [39]

    Anyv2v: A tuning-free framework for any video-to- video editing tasks

    Max Ku, Cong Wei, Weiming Ren, Harry Yang, and Wenhu Chen. Anyv2v: A tuning-free framework for any video-to- video editing tasks. arXiv:2403.14468, 2024. 2

    2024 arXiv

  33. [40]

    Vivid-1-to-3: Novel view synthesis with video diffusion models

    Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, and Kwang Moo Yi. Vivid-1-to-3: Novel view synthesis with video diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6775–6785, 2024. 2

    2024

  34. [41]

    Two algorithms for constructing a delaunay triangulation

    Der-Tsai Lee and Bruce J Schachter. Two algorithms for constructing a delaunay triangulation. International Journal of Computer & Information Sciences, 9(3):219–242, 1980. 5

    1980

  35. [42]

    Video diffusion models are strong video inpainter

    Minhyeok Lee, Suhwan Cho, Chajin Shin, Jungho Lee, Sunghun Yang, and Sangyoun Lee. Video diffusion models are strong video inpainter. arXiv:2408.11402, 2024. 2

    2024 arXiv

  36. [43]

    Differential diffusion: Giving each pixel its strength

    Eran Levin and Ohad Fried. Differential diffusion: Giving each pixel its strength. arXiv:2306.00950, 2023. 8

    2023 arXiv

  37. [44]

    Physics- Based Simulation

    Minchen Li, Chenfanfu Jiang, and Zhaofeng Luo. Physics- Based Simulation. 2024. 4

    2024

  38. [45]

    Animate your motion: Turning still images into dynamic videos

    Mingxiao Li, Bo Wan, Marie-Francine Moens, and Tinne Tuytelaars. Animate your motion: Turning still images into dynamic videos. arXiv:2403.10179, 2024. 3

    2024 arXiv

  39. [46]

    Generative image dynamics

    Zhengqi Li, Richard Tucker, Noah Snavely, and Aleksander Holynski. Generative image dynamics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24142–24153, 2024. 3, 5

    2024

  40. [47]

    Re- conx: Reconstruct any scene from sparse views with video diffusion model

    Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Re- conx: Reconstruct any scene from sparse views with video diffusion model. arXiv:2408.16767, 2024. 2

    2024 arXiv

  41. [48]

    Riganything: Template-free autoregressive rigging for diverse 3d assets

    Isabella Liu, Zhan Xu, Wang Yifan, Hao Tan, Zexiang Xu, Xiaolong Wang, Hao Su, and Zifan Shi. Riganything: Template-free autoregressive rigging for diverse 3d assets. arXiv preprint arXiv:2502.09615, 2025. 2

    2025

  42. [49]

    Physgen: Rigid-body physics-grounded image- to-video generation

    Shaowei Liu, Zhongzheng Ren, Saurabh Gupta, and Shen- long Wang. Physgen: Rigid-body physics-grounded image- to-video generation. In European Conference on Computer Vision, pages 360–378. Springer, 2025. 3

    2025

  43. [50]

    A layered authoring tool for stylized 3d animations

    Jiaju Ma, Li-Yi Wei, and Rubaiat Habib Kazi. A layered authoring tool for stylized 3d animations. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–14, 2022. 3

    2022

  44. [51]

    Cinemo: Consis- tent and controllable image animation with motion diffusion models

    Xin Ma, Yaohui Wang, Gengyu Jia, Xinyuan Chen, Yuan- Fang Li, Cunjian Chen, and Yu Qiao. Cinemo: Consis- tent and controllable image animation with motion diffusion models. arXiv:2407.15642, 2024. 2, 6, 7, 8, 14, 15, 16, 17

    2024 arXiv

  45. [52]

    Digital character animation 3

    George Maestri. Digital character animation 3. New Riders,

  46. [53]

    Towards world simulator: Crafting phys- ical commonsense-based benchmark for video generation

    Fanqing Meng, Jiaqi Liao, Xinyu Tan, Wenqi Shao, Quan- feng Lu, Kaipeng Zhang, Yu Cheng, Dianqi Li, Yu Qiao, and Ping Luo. Towards world simulator: Crafting phys- ical commonsense-based benchmark for video generation. arXiv:2410.05363, 2024. 3

    arXiv

  47. [54]

    Softmax splatting for video frame interpolation

    Simon Niklaus and Feng Liu. Softmax splatting for video frame interpolation. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 5437–5446, 2020. 5 10

    2020

  48. [55]

    Mofa-video: Controllable image animation via generative motion field adaptions in frozen image-to-video diffusion model

    Muyao Niu, Xiaodong Cun, Xintao Wang, Yong Zhang, Ying Shan, and Yinqiang Zheng. Mofa-video: Controllable image animation via generative motion field adaptions in frozen image-to-video diffusion model. arXiv:2405.20222,

    arXiv

  49. [56]

    Sakuga-42m dataset: Scaling up cartoon research

    Zhenglin Pan, Yu Zhu, and Yuxuan Mu. Sakuga-42m dataset: Scaling up cartoon research. arXiv:2405.07425,

    arXiv

  50. [57]

    Autocomplete animated sculpting

    Mengqi Peng, Li-yi Wei, Rubaiat Habib Kazi, and Vladimir G Kim. Autocomplete animated sculpting. In Pro- ceedings of the 33rd Annual ACM Symposium on User Inter- face Software and Technology, pages 760–777, 2020. 3

    2020

  51. [58]

    Sam 2: Seg- ment anything in images and videos

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R¨adle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Seg- ment anything in images and videos. arXiv:2408.00714,

    arXiv

  52. [59]

    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. 2, 3

    2022

  53. [60]

    Learning tem- porally consistent video depth from video diffusion priors

    Jiahao Shao, Yuanbo Yang, Hongyu Zhou, Youmin Zhang, Yujun Shen, Matteo Poggi, and Yiyi Liao. Learning tem- porally consistent video depth from video diffusion priors. arXiv:2406.01493, 2024. 2

    2024 arXiv

  54. [61]

    Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling

    Xiaoyu Shi, Zhaoyang Huang, Fu-Yun Wang, Weikang Bian, Dasong Li, Yi Zhang, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, et al. Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling. In ACM SIGGRAPH 2024 Conference Papers , p...

    2024

  55. [62]

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

    Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. arXiv:2209.14792, 2022. 2

    2022 arXiv

  56. [63]

    Energetically consistent invertible elastic- ity

    Alexey Stomakhin, Russell Howes, Craig A Schroeder, and Joseph M Teran. Energetically consistent invertible elastic- ity. In Symposium on Computer Animation, 2012. 5, 13

    2012

  57. [64]

    Live sketch: Video-driven dynamic deformation of static drawings

    Qingkun Su, Xue Bai, Hongbo Fu, Chiew-Lan Tai, and Jue Wang. Live sketch: Video-driven dynamic deformation of static drawings. In Proceedings of the 2018 chi conference on human factors in computing systems , pages 1–12, 2018. 2

    2018

  58. [66]

    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, 2025. 4

    2025

  59. [67]

    Sv3d: Novel multi-view syn- thesis and 3d generation from a single image using latent video diffusion

    Vikram V oleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view syn- thesis and 3d generation from a single image using latent video diffusion. In European Conference on Computer...

    2025

  60. [68]

    Zero-shot video semantic segmentation based on pre-trained diffusion models

    Qian Wang, Abdelrahman Eldesokey, Mohit Mendiratta, Fangneng Zhan, Adam Kortylewski, Christian Theobalt, and Peter Wonka. Zero-shot video semantic segmentation based on pre-trained diffusion models. arXiv:2405.16947, 2024. 2

    2024 arXiv

  61. [69]

    Motionctrl: A unified and flexible motion controller for video generation

    Zhouxia Wang, Ziyang Yuan, Xintao Wang, Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH 2024 Conference Pa- pers, pages 1–11, 2024. 3

    2024

  62. [70]

    Dreamvideo: Composing your dream videos with customized subject and motion

    Yujie Wei, Shiwei Zhang, Zhiwu Qing, Hangjie Yuan, Zhi- heng Liu, Yu Liu, Yingya Zhang, Jingren Zhou, and Hong- ming Shan. Dreamvideo: Composing your dream videos with customized subject and motion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog...

    2024

  63. [71]

    Secondary motion for performed 2d animation

    Nora S Willett, Wilmot Li, Jovan Popovic, Floraine Berthouzoz, and Adam Finkelstein. Secondary motion for performed 2d animation. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technol- ogy, pages 97–108, 2017. 3

    2017

  64. [72]

    A mixed-initiative interface for animating static pictures

    Nora S Willett, Rubaiat Habib Kazi, Michael Chen, George Fitzmaurice, Adam Finkelstein, and Tovi Grossman. A mixed-initiative interface for animating static pictures. In Proceedings of the 31st annual ACM symposium on user in- terface software and technology, pages 649–661, 2018. 3

    2018

  65. [73]

    Aniclipart: Clipart animation with text-to-video priors

    Ronghuan Wu, Wanchao Su, Kede Ma, and Jing Liao. Aniclipart: Clipart animation with text-to-video priors. arXiv:2404.12347, 2024. 2

    2024 arXiv

  66. [74]

    Draganything: Motion control for any- thing using entity representation

    Weijia Wu, Zhuang Li, Yuchao Gu, Rui Zhao, Yefei He, David Junhao Zhang, Mike Zheng Shou, Yan Li, Tingting Gao, and Di Zhang. Draganything: Motion control for any- thing using entity representation. In European Conference on Computer Vision, pages 331–348. Springer, 2025. 2, 3...

    2025

  67. [75]

    Physgaussian: Physics- integrated 3d gaussians for generative dynamics

    Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, and Chenfanfu Jiang. Physgaussian: Physics- integrated 3d gaussians for generative dynamics. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4389–4398, 2024. 3

    2024

  68. [76]

    Au- tocomplete hand-drawn animations

    Jun Xing, Li-Yi Wei, Takaaki Shiratori, and Koji Yatani. Au- tocomplete hand-drawn animations. ACM Transactions on Graphics (TOG), 34(6):1–11, 2015. 3

    2015

  69. [77]

    Energy-brushes: Inter- active tools for illustrating stylized elemental dynamics

    Jun Xing, Rubaiat Habib Kazi, Tovi Grossman, Li-Yi Wei, Jos Stam, and George Fitzmaurice. Energy-brushes: Inter- active tools for illustrating stylized elemental dynamics. In Proceedings of the 29th Annual Symposium on User Inter- face Software and Technology, pages 755–766, 2...

    2016

  70. [78]

    Tooncrafter: Generative cartoon interpolation

    Jinbo Xing, Hanyuan Liu, Menghan Xia, Yong Zhang, Xin- tao Wang, Ying Shan, and Tien-Tsin Wong. Tooncrafter: Generative cartoon interpolation. arXiv:2405.17933, 2024. 2, 3, 6, 8, 14

    2024 arXiv

  71. [79]

    Dynamicrafter: Animating open-domain images with video diffusion priors

    Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Wangbo Yu, Hanyuan Liu, Gongye Liu, Xintao Wang, Ying Shan, and Tien-Tsin Wong. Dynamicrafter: Animating open-domain images with video diffusion priors. In Eu- 11 ropean Conference on Computer Vision , pages 399–417. Springer, ...

    2025

  72. [80]

    A survey on video diffusion models

    Zhen Xing, Qijun Feng, Haoran Chen, Qi Dai, Han Hu, Hang Xu, Zuxuan Wu, and Yu-Gang Jiang. A survey on video diffusion models. ACM Computing Surveys, 2023. 2

    2023

  73. [81]

    Direct-a-video: Customized video generation with user- directed camera movement and object motion

    Shiyuan Yang, Liang Hou, Haibin Huang, Chongyang Ma, Pengfei Wan, Di Zhang, Xiaodong Chen, and Jing Liao. Direct-a-video: Customized video generation with user- directed camera movement and object motion. In ACM SIG- GRAPH 2024 Conference Papers, pages 1–12, 2024. 3

    2024

  74. [82]

    Dragnuwa: Fine-grained control in video generation by integrating text, image, and trajectory

    Shengming Yin, Chenfei Wu, Jian Liang, Jie Shi, Houqiang Li, Gong Ming, and Nan Duan. Dragnuwa: Fine-grained control in video generation by integrating text, image, and trajectory. arXiv:2308.08089, 2023. 2

    2023 arXiv

  75. [83]

    Complementary dynamics

    Jiayi Eris Zhang, Seungbae Bang, David IW Levin, and Alec Jacobson. Complementary dynamics. arXiv:2009.02462,

    2009 arXiv

  76. [84]

    Adding conditional control to text-to-image diffusion models, 2023

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models, 2023. 3, 5, 14

    2023

  77. [85]

    Physdreamer: Physics-based interac- tion with 3d objects via video generation

    Tianyuan Zhang, Hong-Xing Yu, Rundi Wu, Brandon Y Feng, Changxi Zheng, Noah Snavely, Jiajun Wu, and William T Freeman. Physdreamer: Physics-based interac- tion with 3d objects via video generation. In European Con- ference on Computer Vision, pages 388–406. Springer, 2025. 3

    2025

  78. [86]

    Mimicmo- tion: High-quality human motion video generation with confidence-aware pose guidance

    Yuang Zhang, Jiaxi Gu, Li-Wen Wang, Han Wang, Junqi Cheng, Yuefeng Zhu, and Fangyuan Zou. Mimicmo- tion: High-quality human motion video generation with confidence-aware pose guidance. arXiv:2406.19680, 2024. 2

    2024 arXiv

  79. [87]

    Tora: Trajectory-oriented diffusion trans- former for video generation

    Zhenghao Zhang, Junchao Liao, Menghao Li, Long Qin, and Weizhi Wang. Tora: Trajectory-oriented diffusion trans- former for video generation. arXiv:2407.21705, 2024. 3

    2024 arXiv

  80. [88]

    Trip: Temporal resid- ual learning with image noise prior for image-to-video dif- fusion models

    Zhongwei Zhang, Fuchen Long, Yingwei Pan, Zhaofan Qiu, Ting Yao, Yang Cao, and Tao Mei. Trip: Temporal resid- ual learning with image noise prior for image-to-video dif- fusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages...

    2024

  81. [89]

    Clothes sway gently on a line in a light breeze

    Rui Zhao, Yuchao Gu, Jay Zhangjie Wu, David Junhao Zhang, Jiawei Liu, Weijia Wu, Jussi Keppo, and Mike Zheng Shou. Motiondirector: Motion customization of text-to- video diffusion models. arXiv:2310.08465, 2023. 3 12 Appendix A. Motion Equation To model the fluidity and exagge...

    2023 arXiv

Pith tools

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