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arxiv: 2604.20539 · v1 · submitted 2026-04-22 · 💻 cs.GR

Recognition: unknown

Animator-Centric Skeleton Generation on Objects with Fine-Grained Details

Bin Huang, Chaoyue Song, Cheng zeng, Jiansong Pei, Junhao Chen, Mingze Sun, Ruqi Huang, Shaohui Wang, Tianyuan Chang, Zijiao Zeng

Authors on Pith no claims yet

Pith reviewed 2026-05-09 22:45 UTC · model grok-4.3

classification 💻 cs.GR
keywords skeleton generation3D rigginganimator-centricsemantic tokenizationrigged meshesbone density controlauto-regressive modelingdeep learning for animation
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The pith

A framework using semantic tokenization and density control generates high-quality skeletons for complex 3D objects while allowing direct animator control.

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

Current deep learning approaches to skeleton generation struggle with the structural complexity of modern 3D models and provide little controllability for users. This paper introduces an animator-centric method that first assembles a dataset of over eighty thousand rigged meshes. It then uses a semantic-aware tokenization scheme to break bones into meaningful groups for auto-regressive prediction, plus a module that lets users adjust bone density directly. These elements together aim to produce accurate skeletons on difficult inputs while meeting two key needs expressed by professional animators. If the approach works as claimed, animation pipelines could move past the current bottleneck in rigging detailed assets.

Core claim

The framework curates a large-scale dataset of 82,633 rigged meshes and employs a semantic-aware tokenization scheme for auto-regressive modeling that subdivides bones into semantically meaningful groups to enhance robustness, along with a learnable density interval module for control, resulting in high-quality skeleton generation on challenging inputs that fulfills critical animator requirements.

What carries the argument

Semantic-aware tokenization scheme that subdivides bones into semantically meaningful groups for auto-regressive modeling, paired with a learnable density interval module for soft control over bone density.

If this is right

  • Produces high-quality skeletons for objects with complicated structures.
  • Provides intuitive control handles to animators.
  • Allows soft, direct control over bone density via the density module.
  • Complements purely geometric prior methods through semantic grouping.
  • Fulfills two critical requirements from professional animators.

Where Pith is reading between the lines

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

  • The tokenization approach might be adapted to other 3D generative tasks involving part-based structures.
  • Professional animators could use the density control to quickly prototype different skeleton densities for the same model.
  • If the dataset captures sufficient diversity, the method could reduce the need for manual skeleton editing in production.
  • Extending the framework to handle dynamic or deforming meshes remains an open direction suggested by the focus on static rigged objects.

Load-bearing premise

The dataset of 82,633 rigged meshes captures the structural diversity and semantic groupings required for the tokenization to generalize to new complex models.

What would settle it

Evaluating the generated skeletons on a held-out collection of rigged meshes with fine-grained details, measuring agreement with expert animator skeletons and the usability of the control features.

Figures

Figures reproduced from arXiv: 2604.20539 by Bin Huang, Chaoyue Song, Cheng zeng, Jiansong Pei, Junhao Chen, Mingze Sun, Ruqi Huang, Shaohui Wang, Tianyuan Chang, Zijiao Zeng.

Figure 1
Figure 1. Figure 1: We propose an automatic and controllable skeleton generation framework. Given an input mesh, our method generates fine [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: We compare the bone numbers distribution of our dataset with that of existing open-source datasets. (a) The dataset size and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The overall pipeline of our framework. Given an input, the model first extracts geometric embeddings through a shape encoder. We introduce semantic-based skeleton tokenization through a semantic understanding model. A Learnable density token and a CLS token are added to realize controllable skeleton generation. shapes for testing, ensuring that the training and testing sets share similar distribution chara… view at source ↗
Figure 4
Figure 4. Figure 4: Our semantic-based skeleton tokenization. (a) illustrates [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of skeleton generation results on our test set, and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Main bones control results. Our model can generate the [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: Density control results. Our model enables the genera [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Skeleton generation is essential for animating 3D assets, but current deep learning methods remain limited: they cannot handle the growing structural complexity of modern models and offer minimal controllability, creating a major bottleneck for real-world animation workflows. To address this, we propose an animator-centric SG framework that achieves high-quality skeleton prediction on complex inputs while providing intuitive control handles. Our contributions are threefold. First, we curate a large-scale dataset of 82,633 rigged meshes with diverse and complicated structures. Second, we introduce a novel semantic-aware tokenization scheme for auto-regressive modeling. This scheme effectively complements purely geometric prior methods by subdividing bones into semantically meaningful groups, thereby enhancing robustness to structural complexity and enabling a key control mechanism. Third, we design a learnable density interval module that allows animators to exert soft, direct control over bone density. Extensive experiments demonstrate that our framework not only generates high-quality skeletons for challenging inputs but also successfully fulfills two critical requirements from professional animators.

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

2 major / 2 minor

Summary. The paper proposes an animator-centric skeleton generation framework for 3D objects with fine-grained details. It curates a dataset of 82,633 rigged meshes, introduces a semantic-aware tokenization scheme for auto-regressive modeling that subdivides bones into semantically meaningful groups to complement geometric priors, and designs a learnable density interval module for soft control over bone density. The central claim is that the framework generates high-quality skeletons for challenging inputs while fulfilling two critical requirements from professional animators, as shown by extensive experiments.

Significance. If the empirical claims hold with proper validation, the work could meaningfully advance skeleton generation in computer graphics by addressing structural complexity and adding animator-controllable mechanisms, potentially easing bottlenecks in animation pipelines. The scale of the curated dataset and the tokenization approach represent potential strengths for generalization if they are shown to capture semantic groupings beyond memorization.

major comments (2)
  1. [Abstract] Abstract: The claim that 'extensive experiments demonstrate that our framework not only generates high-quality skeletons for challenging inputs but also successfully fulfills two critical requirements from professional animators' provides no quantitative metrics, baselines, error bars, ablation studies, or details on how the two animator requirements were measured or evaluated. This absence is load-bearing for the central claim of success and controllability.
  2. [Dataset and tokenization description] Dataset curation and semantic-aware tokenization sections: The semantic-aware tokenization scheme is presented as effectively complementing geometric priors by subdividing bones into meaningful groups, but the manuscript supplies only the dataset size (82,633 rigged meshes) with no curation criteria, semantic taxonomy, coverage statistics, diversity analysis, or hold-out tests on fine-grained structures. This leaves the generalization assumption unverified and risks the scheme merely memorizing the training distribution rather than enabling robust control.
minor comments (2)
  1. [Abstract] The abstract mentions 'two critical requirements from professional animators' without enumerating them; adding a brief explicit list would improve clarity for readers.
  2. [Method] Notation for the learnable density interval module and tokenization scheme could be more precisely defined (e.g., explicit equations for interval sampling and group subdivision) to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The comments highlight important areas where the manuscript can be strengthened for clarity and verifiability. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'extensive experiments demonstrate that our framework not only generates high-quality skeletons for challenging inputs but also successfully fulfills two critical requirements from professional animators' provides no quantitative metrics, baselines, error bars, ablation studies, or details on how the two animator requirements were measured or evaluated. This absence is load-bearing for the central claim of success and controllability.

    Authors: We agree that the abstract is too high-level and does not sufficiently substantiate the central claims. In the revised version, we will rewrite the abstract to incorporate key quantitative results from the experiments, including specific performance metrics against baselines, error bars for reported values, references to ablation studies, and explicit details on the evaluation protocol for the two animator requirements (e.g., the design of any user studies or quantitative proxies used). These additions will be drawn from the existing experimental section while ensuring the abstract remains concise. We will also cross-reference the main text for full methodological details on controllability and quality assessment. revision: yes

  2. Referee: [Dataset and tokenization description] Dataset curation and semantic-aware tokenization sections: The semantic-aware tokenization scheme is presented as effectively complementing geometric priors by subdividing bones into meaningful groups, but the manuscript supplies only the dataset size (82,633 rigged meshes) with no curation criteria, semantic taxonomy, coverage statistics, diversity analysis, or hold-out tests on fine-grained structures. This leaves the generalization assumption unverified and risks the scheme merely memorizing the training distribution rather than enabling robust control.

    Authors: We acknowledge that the current manuscript provides insufficient detail on dataset curation and the semantic-aware tokenization process. We will expand the relevant sections to include: (1) explicit curation criteria and sources for the 82,633 rigged meshes, (2) the semantic taxonomy employed for bone grouping, (3) coverage statistics and diversity analysis across object categories and structural complexities, and (4) results from hold-out evaluations specifically targeting fine-grained structures. These additions will be supported by new figures or tables as needed. Regarding the risk of memorization, we will strengthen the experimental discussion to show how the tokenization complements geometric priors through controlled comparisons and generalization tests, thereby clarifying the mechanism for robust control. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation is self-contained

full rationale

The paper's core claims rest on curating an external dataset of 82,633 rigged meshes, proposing a semantic-aware tokenization scheme for auto-regressive modeling, and adding a learnable density interval module. These are presented as independent contributions trained and evaluated on the dataset, with no equations, predictions, or uniqueness arguments that reduce by construction to fitted parameters or self-citations. The framework is described as generalizing from the curated data rather than deriving outputs tautologically from its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the existence and quality of the 82,633-mesh dataset plus the effectiveness of the semantic grouping and density module; no explicit free parameters, axioms, or invented entities are detailed beyond standard deep-learning assumptions.

pith-pipeline@v0.9.0 · 5498 in / 1149 out tokens · 149569 ms · 2026-05-09T22:45:48.791879+00:00 · methodology

discussion (0)

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