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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
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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.

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Reference graph

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