pith. sign in

arxiv: 2606.01518 · v2 · pith:QXXMV7WQnew · submitted 2026-06-01 · 💻 cs.CV · cs.GR

SkelMo: Universal Skeletal Motion Generation for 3D Rigged Shapes

Ye Tao , Yuxin Yao , Kendong Liu , Dapeng Wu , Junhui Hou This is my paper

Pith reviewed 2026-06-30 11:05 UTC · model grok-4.3

classification 💻 cs.CV cs.GR
keywords skeletal motion generationrigged 3D shapesdiffusion models2D video to 3D animationcategory-agnostic generationstructural-semantic injection4D asset generation
0
0 comments X

The pith

A diffusion model with structural-semantic injection generates skeletal animations for arbitrary rigged 3D shapes from 2D video guidance.

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

The paper presents SkelMo as a way to produce skeletal motions for 3D rigged shapes without relying on fixed templates or expensive per-shape optimization. It addresses the lack of training data by building a dataset of around 20,000 diverse animations that include full textures, rigs, and sequences. A structural-semantic injection step adds texture and semantic information to joint representations so that 2D motion cues from video can be mapped onto different joint hierarchies. If the method holds, it would let users create consistent 4D animations for new biological or imaginary shapes directly from ordinary video footage.

Core claim

SkelMo is a diffusion-based framework for category-agnostic skeletal animation generation from 2D video guidance. To overcome data scarcity the authors curate a dataset of approximately 20,000 3D animations with textures, rigging, and varied sequences. The structural-semantic injection mechanism integrates texture and semantic attributes directly into skeletal joint representations, enabling the model to map perceived visual dynamics to specific joint hierarchies and functional roles. This produces high-fidelity animations that preserve anatomical consistency across unseen categories ranging from biological species to fantastical beings.

What carries the argument

The structural-semantic injection mechanism, which adds texture and semantic attributes to skeletal joint representations to map 2D visual motion cues onto heterogeneous 3D joint hierarchies.

If this is right

  • The approach synthesizes animations that maintain anatomical consistency for both existing species and fantastical beings.
  • It removes the need for category-specific templates or per-case optimization loops.
  • The model outperforms prior template-based and optimization-based methods on standard benchmarks for skeletal motion quality.
  • It supports efficient production of 4D assets by converting ordinary video into rigged animations without manual intervention.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the injection mechanism generalizes, it could support motion retargeting between rigs that differ in topology without additional training data.
  • The same joint-representation technique might be tested on video inputs that contain partial occlusions or multiple interacting characters.
  • Extending the dataset curation process to include procedural variations in joint counts could further stress-test the category-agnostic claim.

Load-bearing premise

The structural-semantic injection mechanism successfully bridges the kinematic gap between 2D visual motion cues and heterogeneous 3D skeletal structures across unseen categories.

What would settle it

A test set of rigged shapes from categories absent in the 20,000-animation dataset where generated motions produce anatomically inconsistent joint angles or non-functional limb trajectories when driven by the same 2D video input.

Figures

Figures reproduced from arXiv: 2606.01518 by Dapeng Wu, Junhui Hou, Kendong Liu, Ye Tao, Yuxin Yao.

Figure 1
Figure 1. Figure 1: MotionDreamer utilizes topology agnostic diffusion to generate skeletal anima￾tion according to the given skeleton and driving video. Abstract. Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by spe￾cific topologies and fail to generalize across diverse morphologies. Con￾versely, per-case optimization is computationally expensive, s… view at source ↗
Figure 2
Figure 2. Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of MotionDreamer Pipeline. Given a mesh M with its skeleton S 0 and driving video X , our approach follows a structured pipeline: At each sampling step t, the driving video X is encoded using DINOv2 Encoder, and fed into the video branch. Simultaneously, the initial skeleton S 0 is projected into the latent space as z 0 via a linear layer. This condition is then concatenated with the current noisy… view at source ↗
Figure 4
Figure 4. Figure 4: Visual comparison between our MotionDreamer and other state-of-the-art methods. For each example, the left side displays the rendered 3D mesh, while the right side shows its corresponding skeletal structure. Effect of Texture-Semantic Injection. We investigate the impact of the texture-semantic injection module described in Sec. 4.2. As shown in [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visual comparison of different model variants. From left to right: (a) Results without texture-semantic injection. (b) Results without bidirectional video-skeleton fusion. (c) Results using relative skeletal representations instead of global coordinates. (d) Results of our full model. Each column presents the generated skeletal poses for different input characters and driving frames. with the driving frame… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of cross-category motion transfer results. Each row represents a specific transfer case across multiple frames. The leftmost column displays the target static meshes, while the subsequent columns show pairs of the driving video (source motion) and the resulting target skeleton. The first and second rows demonstrate intra￾category transfer (human-to-human and quadruped-to-quadruped, respective… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of cross-category motion transfer results. Each row represents a specific transfer case across multiple frames. The leftmost column displays the target static meshes, while the subsequent columns show pairs of the driving video (source motion) and the resulting target skeleton. The first and second rows demonstrate intra￾category transfer (human-to-human and quadruped-to-quadruped, respective… view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of in-the-wild scenarios. Our method demonstrates strong gen￾eralization across diverse driving video sources. The first row showcases results driven by AI-generated videos (e.g., from Doubao AI), featuring stylized motions. The second row illustrates performance on real-world captures [34], highlighting the stability of the model under complex lighting and backgrounds. In both cases, high-fi… view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of in-the-wild scenarios. Our method demonstrates strong gen￾eralization across diverse driving video sources. The first row showcases results driven by AI-generated videos (e.g., from Doubao AI), featuring stylized motions. The second row illustrates performance on real-world captures [36], highlighting the stability of the model under complex lighting and backgrounds. In both cases, high-fi… view at source ↗
read the original abstract

Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by specific topologies and fail to generalize across diverse morphologies. Conversely, per-case optimization is computationally expensive, susceptible to local optima, and highly sensitive to viewpoint-induced ambiguities. In this paper, we present SkelMo, a diffusion-based framework designed for category-agnostic skeletal animation generation from 2D video guidance. To overcome the scarcity of high-quality training data, we have curated a large-scale dynamic dataset comprising approximately 20,000 diverse 3D animations, each featuring complete textures, skeletal rigging, and a wide array of comprehensive animation sequences. To bridge the kinematic gap between 2D visual motion cues and heterogeneous 3D skeletal structures, we propose a structural-semantic injection mechanism. Our model integrates texture and semantic attributes directly into skeletal joint representations. This allows it to map perceived visual dynamics to specific joint hierarchies and their functional roles. This enables SkelMo to synthesize high-fidelity animations that maintain anatomical consistency across a vast range of unseen categories, from existing biological species to fantastical beings. Extensive experiments demonstrate that our approach significantly outperforms existing methods, setting a new state-of-the-art benchmark for robust and efficient 4D asset generation. Project Page: https://research.davytao.me/skelmo/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper introduces SkelMo, a diffusion-based framework for category-agnostic skeletal animation generation from 2D video guidance for 3D rigged shapes. It curates a large-scale dataset of approximately 20,000 diverse 3D animations with textures, rigging, and sequences, and proposes a structural-semantic injection mechanism that integrates texture and semantic attributes into skeletal joint representations to map 2D visual dynamics to heterogeneous 3D joint hierarchies. The work claims to synthesize high-fidelity, anatomically consistent animations across unseen categories and to significantly outperform existing methods, establishing a new state-of-the-art for robust and efficient 4D asset generation.

Significance. If the central claims hold, the work would be significant for scalable 4D content creation by removing reliance on fixed templates or per-instance optimization. The curation of a 20k-animation dataset with full rigging and textures represents a concrete resource contribution that could support future research in cross-category motion transfer.

minor comments (2)
  1. The abstract states that 'extensive experiments demonstrate' outperformance and a new SOTA but provides no metrics, baselines, or evaluation protocol; this prevents verification of the performance claim.
  2. No architecture diagram, loss formulation, or training details are visible in the supplied text, making it impossible to assess the structural-semantic injection mechanism or its claimed ability to bridge the 2D-to-3D kinematic gap.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and summary of our work. The report notes potential significance but gives an uncertain recommendation without listing any specific major comments. We therefore have no individual points to rebut point-by-point and stand ready to supply additional evidence or clarifications should the referee identify particular concerns about the claims, experiments, or dataset.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an empirical ML framework (diffusion model with structural-semantic injection) trained on a curated dataset of ~20k animations. No derivation chain, equations, or first-principles predictions are presented that reduce to fitted inputs or self-citations by construction. Claims rest on experimental outperformance rather than tautological mappings. The abstract and description contain no load-bearing self-referential steps of the enumerated kinds.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based on abstract only; no specific free parameters, axioms, or invented entities can be identified from the provided information.

pith-pipeline@v0.9.1-grok · 5782 in / 1046 out tokens · 36676 ms · 2026-06-30T11:05:11.292420+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

45 extracted references · 7 canonical work pages · 3 internal anchors

  1. [1]

    Auto-rig pro.https://superhivemarket.com/products/auto-rig-pro, blender add-on for character rigging and animation retargeting

  2. [2]

    Qwen Technical Report

    Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  3. [3]

    ByteDance: Doubao.https://www.doubao.com/(2026), accessed: 2026-03-05

    2026

  4. [4]

    In: CVPR (2026)

    Chen, H., Chen, X., Zhang, Y., Xu, Z., Chen, A.: Motion 3-to-4: 3d motion recon- struction for 4d synthesis. In: CVPR (2026)

    2026

  5. [5]

    In: CVPR

    Choi, H., Moon, G., Chang, J.Y., Lee, K.M.: Beyond static features for temporally consistent 3d human pose and shape from a video. In: CVPR. pp. 1964–1973 (2021)

    1964

  6. [6]

    In: CVPR

    Chu, R., Liu, Z., Ye, X., Tan, X., Qi, X., Fu, C.W., Jia, J.: Command-driven artic- ulated object understanding and manipulation. In: CVPR. pp. 8813–8823 (2023)

    2023

  7. [7]

    NIPS36, 35799–35813 (2023)

    Deitke, M., Liu, R., Wallingford, M., Ngo, H., Michel, O., Kusupati, A., Fan, A., Laforte, C., Voleti, V., Gadre, S.Y., et al.: Objaverse-xl: A universe of 10m+ 3d objects. NIPS36, 35799–35813 (2023)

    2023

  8. [8]

    In: CVPR

    Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsani, K., Kembhavi, A., Farhadi, A.: Objaverse: A universe of annotated 3d objects. In: CVPR. pp. 13142–13153 (2023)

    2023

  9. [9]

    In: SIGGRAPH

    Gat, I., Raab, S., Tevet, G., Reshef, Y., Bermano, A.H., Cohen-Or, D.: Anytop: Character animation diffusion with any topology. In: SIGGRAPH. pp. 1–10 (2025)

    2025

  10. [10]

    In: CVPR

    Gong,K.,Wen,Z.,He,W.,Xu,M.,Wang,Q.,Zhang,N.,Li,Z.,Lian,D.,Zhao,W., He, X., et al.: Mocapanything: Unified 3d motion capture for arbitrary skeletons from monocular videos. In: CVPR. pp. 7089–7099 (2026)

    2026

  11. [11]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7122–7131 (2018)

    2018

  12. [12]

    In: ECCV

    Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: ECCV. pp. 371–386 (2018)

    2018

  13. [13]

    In: CVPR

    Kocabas, M., Athanasiou, N., Black, M.J.: Vibe: Video inference for human body pose and shape estimation. In: CVPR. pp. 5253–5263 (2020)

    2020

  14. [14]

    Kocabas, M., Yuan, Y., Molchanov, P., Guo, Y., Black, M.J., Hilliges, O., Kautz, J., Iqbal, U.: Pace: Human and camera motion estimation from in-the-wild videos. In: 3DV. pp. 397–408. IEEE (2024)

    2024

  15. [15]

    In: ICCV

    Li, Z., Luo, M., Hou, R., Zhao, X., Liu, H., Chang, H., Liu, Z., Li, C.: Morph: A motion-free physics optimization framework for human motion generation. In: ICCV. pp. 14580–14589 (2025)

    2025

  16. [16]

    In: CVPR

    Lin, J., Zeng, A., Wang, H., Zhang, L., Li, Y.: One-stage 3d whole-body mesh recovery with component aware transformer. In: CVPR. pp. 21159–21168 (2023)

    2023

  17. [17]

    TOG44(4), 1–12 (2025)

    Liu, I., Xu, Z., Yifan, W., Tan, H., Xu, Z., Wang, X., Su, H., Shi, Z.: Riganything: Template-free autoregressive rigging for diverse 3d assets. TOG44(4), 1–12 (2025)

    2025

  18. [18]

    In: Seminal Graphics Papers: Pushing the Boundaries, Volume 2, pp

    Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: Smpl: A skinned multi-person linear model. In: Seminal Graphics Papers: Pushing the Boundaries, Volume 2, pp. 851–866 (2023)

    2023

  19. [19]

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H.V., Szafraniec, M., Khalidov, V., Fernandez,P.,Haziza,D.,Massa,F.,El-Nouby,A.,Howes,R.,Huang,P.Y.,Xu,H., Sharma, V., Li, S.W., Galuba, W., Rabbat, M., Assran, M., Ballas, N., Synnaeve, G., Misra, I., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: Dinov2: Learning robust visual features without ...

    2023

  20. [20]

    In: CVPR

    Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A.A., Tzionas, D., Black, M.J.: Expressive body capture: 3d hands, face, and body from a single image. In: CVPR. pp. 10975–10985 (2019)

    2019

  21. [21]

    arXiv preprint arXiv:2312.17142 (2023)

    Ren, J., Pan, L., Tang, J., Zhang, C., Cao, A., Zeng, G., Liu, Z.: Dreamgaussian4d: Generative 4d gaussian splatting. arXiv preprint arXiv:2312.17142 (2023)

    work page arXiv 2023

  22. [22]

    CVPR (2026)

    Sabathier, R., Novotny, D., Mitra, N.J., Monnier, T.: Actionmesh: Animated 3d mesh generation with temporal 3d diffusion. CVPR (2026)

    2026

  23. [23]

    arXiv preprint arXiv:2506.07489 (2025)

    Shi, Y., Liu, Y., Wu, Y., Liu, X., Zhao, C., Luo, J., Zhou, B.: Drive any mesh: 4d latent diffusion for mesh deformation from video. arXiv preprint arXiv:2506.07489 (2025)

    work page arXiv 2025

  24. [24]

    NIPS38, 72152–72184 (2026)

    Song, C., Li, X., Yang, F., Xu, Z., Wei, J., Liu, F., Feng, J., Lin, G., Zhang, J.: Puppeteer: Rig and animate your 3d models. NIPS38, 72152–72184 (2026)

    2026

  25. [25]

    In: CVPR

    Song, C., Zhang, J., Li, X., Yang, F., Chen, Y., Xu, Z., Liew, J.H., Guo, X., Liu, F., Feng, J., et al.: Magicarticulate: Make your 3d models articulation-ready. In: CVPR. pp. 15998–16007 (2025)

    2025

  26. [26]

    In: ECCV

    Sun, K., Litvak, D., Zhang, Y., Li, H., Wu, J., Wu, S.: Ponymation: Learning articulated 3d animal motions from unlabeled online videos. In: ECCV. pp. 100–

  27. [27]

    In: CVPR

    Sun, Q., Wang, Y., Zeng, A., Yin, W., Wei, C., Wang, W., Mei, H., Leung, C.S., Liu, Z., Yang, L., et al.: Aios: All-in-one-stage expressive human pose and shape estimation. In: CVPR. pp. 1834–1843 (2024)

    2024

  28. [28]

    Wan: Open and Advanced Large-Scale Video Generative Models

    Wan, T., Wang, A., Ai, B., Wen, B., Mao, C., Xie, C.W., Chen, D., Yu, F., Zhao, H., Yang, J., Zeng, J., Wang, J., Zhang, J., Zhou, J., Wang, J., Chen, J., Zhu, K., Zhao, K., Yan, K., Huang, L., Feng, M., Zhang, N., Li, P., Wu, P., Chu, R., Feng, R., Zhang, S., Sun, S., Fang, T., Wang, T., Gui, T., Weng, T., Shen, T., Lin, W., Wang, W., Wang, W., Zhou, W.,...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  29. [29]

    In: ECCV

    Wang, Y., Wang, Z., Liu, L., Daniilidis, K.: Tram: Global trajectory and motion of 3d humans from in-the-wild videos. In: ECCV. pp. 467–487. Springer (2024)

    2024

  30. [30]

    In: CVPR

    Wu, S., Li, R., Jakab, T., Rupprecht, C., Vedaldi, A.: Magicpony: Learning artic- ulated 3d animals in the wild. In: CVPR. pp. 8792–8802 (2023)

    2023

  31. [31]

    ICCV (2025)

    Wu, Z., Yu, C., Wang, F., Bai, X.: Animateanymesh: A feed-forward 4d foundation model for text-driven universal mesh animation. ICCV (2025)

    2025

  32. [32]

    In: ICCV

    Xiao, L., Lu, S., Pi, H., Fan, K., Pan, L., Zhou, Y., Feng, Z., Zhou, X., Peng, S., Wang, J.: Motionstreamer: Streaming motion generation via diffusion-based autoregressive model in causal latent space. In: ICCV. pp. 10086–10096 (2025)

    2025

  33. [33]

    In: CVPR

    Xie, T., Chen, Y., Guo, Y., Yang, Y., Zhou, B., Terzopoulos, D., Jiang, Y., Jiang, C.: Animimic: Imitating 3d animation from video priors. In: CVPR. pp. 40266– 40276 (2026)

    2026

  34. [34]

    arXiv preprint arXiv:2407.17470 (2024)

    Xie, Y., Yao, C.H., Voleti, V., Jiang, H., Jampani, V.: Sv4d: Dynamic 3d con- tent generation with multi-frame and multi-view consistency. arXiv preprint arXiv:2407.17470 (2024)

    work page arXiv 2024

  35. [35]

    In: CVPR

    Yang, G., Sun, D., Jampani, V., Vlasic, D., Cole, F., Chang, H., Ramanan, D., Freeman, W.T., Liu, C.: Lasr: Learning articulated shape reconstruction from a monocular video. In: CVPR. pp. 15980–15989 (2021)

    2021

  36. [36]

    In: CVPR (2022) 18 Y

    Yang, G., Vo, M., Neverova, N., Ramanan, D., Vedaldi, A., Joo, H.: Banmo: Build- ing animatable 3d neural models from many casual videos. In: CVPR (2022) 18 Y. Tao et al

    2022

  37. [37]

    CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

    Yang, Z., Teng, J., Zheng, W., Ding, M., Huang, S., Xu, J., Yang, Y., Hong, W., Zhang, X., Feng, G., et al.: Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  38. [38]

    NIPS35, 15296–15308 (2022)

    Yao, C.H., Hung, W.C., Li, Y., Rubinstein, M., Yang, M.H., Jampani, V.: Lassie: Learning articulated shapes from sparse image ensemble via 3d part discovery. NIPS35, 15296–15308 (2022)

    2022

  39. [39]

    In: ICCV

    Yao, C.H., Xie, Y., Voleti, V., Jiang, H., Jampani, V.: Sv4d 2.0: Enhancing spatio- temporal consistency in multi-view video diffusion for high-quality 4d generation. In: ICCV. pp. 13248–13258 (2025)

    2025

  40. [40]

    ICLR (2026)

    Yenphraphai, J., Mirzaei, A., Chen, J., Zou, J., Tulyakov, S., Yeh, R.A., Wonka, P., Wang, C.: Shapegen4d: Towards high quality 4d shape generation from videos. ICLR (2026)

    2026

  41. [41]

    In: ECCV

    Yi, H., Thies, J., Black, M.J., Peng, X.B., Rempe, D.: Generating human interac- tion motions in scenes with text control. In: ECCV. pp. 246–263. Springer (2024)

    2024

  42. [42]

    In: ICCV

    Zhang, B., Xu, S., Wang, C., Yang, J., Zhao, F., Chen, D., Guo, B.: Gaussian variation field diffusion for high-fidelity video-to-4d synthesis. In: ICCV. pp. 12502– 12513 (2025)

    2025

  43. [43]

    NIPS37, 15272–15295 (2024)

    Zhang, H., Chen, X., Wang, Y., Liu, X., Wang, Y., Qiao, Y.: 4diffusion: Multi-view video diffusion model for 4d generation. NIPS37, 15272–15295 (2024)

    2024

  44. [44]

    arXiv preprint arXiv:2503.06955 (2025)

    Zhang, Z., Wang, Y., Mao, W., Li, D., Zhao, R., Wu, B., Song, Z., Zhuang, B., Reid, I., Hartley, R.: Motion anything: Any to motion generation. arXiv preprint arXiv:2503.06955 (2025)

    work page arXiv 2025

  45. [45]

    In: CVPR

    Zuffi, S., Kanazawa, A., Jacobs, D.W., Black, M.J.: 3d menagerie: Modeling the 3d shape and pose of animals. In: CVPR. pp. 6365–6373 (2017)

    2017