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

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.

• The paper proposes an animator-centric framework integrating semantic tokenization and learnable density controls to generate detailed 3D skeletons.
• It employs a conditional sequence modeling approach with a Transformer-based decoder and a curated dataset of over 82k rigged meshes, outperforming previous methods on precision and F1 metrics.
• By enabling explicit animator intervention through main bone conditioning and density control, the method streamlines production-level rigging workflows.

Animator-Centric Skeleton Generation on Fine-Grained 3D Objects

Introduction

The paper "Animator-Centric Skeleton Generation on Objects with Fine-Grained Details" (2604.20539) introduces an integrated, controllable, and high-fidelity framework for automatic 3D skeleton generation tailored towards professional animation pipelines. The proposed system responds to pressing limitations of prior automatic skeleton generation approaches: their inability to capture increasingly complex 3D model topologies and the lack of intuitive, fine-grained control for animators. Unlike existing methods, this work explicitly incorporates part-level semantics and provides mechanisms for explicit animator intervention, bridging the gap between scalable data-driven generation and real-world, production-level character rigging demands.

Dataset Curation and Statistical Analysis

A core asset of this work is a large-scale, systematically curated dataset comprising 82,633 high-quality rigged meshes. The dataset offers a significant leap in diversity and structural complexity, spanning humanoids, tetrapods, birds, aquatic entities, vehicles, and weapons, with bone counts ranging from 5 to 400 per instance. Compared to previous collections such as ModelResource and Articulation-XL, the dataset substantially extends the support for non-humanoid topologies and high-fidelity articulated structures.

Figure 1: The custom dataset exhibits a broader bone count distribution and category diversity than precedent datasets, illustrated alongside exemplar skeleton-mesh pairs.

This breadth not only supports modern animation requirements but also enables effective generalization and robustness evaluation. Stratified sampling ensures training and test splits are statistically representative across count and category axes.

Semantic-Aware and Density-Controllable Auto-Regressive Modeling

The paper formulates the skeleton generation problem as a conditional sequence modeling task: given a 3D mesh, predict a hierarchical set of joint positions and bone connections as discretized tokens. The architecture is rooted in decoder-only Transformer models (OPT-350M backbone), enhanced through cross-attention conditioning on geometric embeddings, part-classification, and explicit user controls.

Figure 2: The overall pipeline encodes geometry, semantic part tokens, density preference, and class handles for controllable skeleton generation.

Semantic Grouping and Tokenization

A distinguishing contribution is the semantic-based skeleton tokenization. A GraphTransformer-based module, trained on 10,000 manually annotated skeletons, predicts semantic part labels for joints (e.g., head, limbs, hair, clothing, wings). This facilitates explicit grouping and DFS-based intra-group ordering, breaking the ambiguity and redundancy associated with naive BFS orderings common in prior art.

Figure 3: The semantic-based tokenization groups the skeleton hierarchically by semantics and applies ordered traversal within each group.

Semantic grouping enables part-level conditioning: supplying coarse main skeletons at test time constrains auxiliary bone generation, essential for production settings where main bones are templated and accessories must be inferred contextually.

Learnable Density Controls

To enable animator-level control over skeleton granularity (i.e., bone count), the model introduces a learnable density interval module. The framework discretizes possible bone counts into $K$ adaptive bins, learning their cutpoints via a monotonic, soft differentiable process. Each bin is associated with an embedding; the model conditions auto-regressive decoding on soft or hard combinations of these based on the user’s density preference. This mechanism supports both sparse and densely-articulated outputs on demand, with the learned bins exhibiting strong correspondence to actual rigging practices.

Empirical Evaluation

Quantitative Results: On the curated dataset, the framework is rigorously evaluated against established baselines (UniRig, Puppeteer, MagicArticulate)—both in retrained and direct inference configurations. Across metrics such as precision, recall, F1, and various skeleton-level Chamfer Distances, the proposed method consistently surpasses all peers. Particularly, the method demonstrates a five- to nine-fold precision/F1 boost over methods unexposed to high-complexity data and maintains considerable improvements over retrained state-of-the-art transformers, especially in auxiliary bone detail and connectivity.

Figure 4: Skeletons generated by the proposed method are structurally faithful and handle complex fine-grained details absent from competing methods.

Ablation Studies: Removing density or class tokens degrades both accuracy and joint-to-joint correspondence, validating each design contribution. Similarly, switching from semantic grouping to global DFS/BFS tokenization results in higher geometric and topological error rates. The advantage of semantic grouping is especially clear when generating regionally-conditional auxiliary bones.

Applications and Animator-Centric Controls

Density-Controllable Skeleton Generation

Animators can select among low, medium, or high bone densities during inference. As demonstrated, increasing density tokens enhances fine detail in a context-appropriate fashion: main bones stay consistent, while more plausible secondary structures are synthesized (e.g., cloth, hair, ribbons, appendages).

Figure 5: By manipulating the density control token, the model generates skeletons with adjustable complexity matched to animators' needs.

Main Bone Conditional Generation

The semantic tokenization mechanism allows for precise conditional completion: given a set of main skeletons (supplied as tokenized embeddings), the model hallucinates consistent, high-quality auxiliary bones. This flexibility is difficult to achieve with geometry-only models, supporting use cases such as garment/accessory rigging atop reusable base skeletons—critical for large animation pipelines.

Figure 6: Conditioned on main bones, the model generates plausible, detailed auxiliary skeletal structures aligned with animator intentions.

Implications and Future Work

Practically, this animator-centric approach markedly reduces manual rigging overhead for high-fidelity assets and adapts seamlessly to the production workflows where animator intervention or specific rigging templates are the norm. Theoretically, the methodology establishes a blueprint for integrating semantic understanding and fine-grained conditional control into autoregressive generative pipelines for complex structured outputs beyond the domain of 3D animation.

The limitations highlight relative underrepresentation of certain non-humanoid categories and the absence of precise local region density controls. Future research is likely to integrate locality-aware generation, context-sensitive editing (region-specific density modulation), and direct integration with downstream animation synthesis modules.

Conclusion

This work establishes an effective, scalable paradigm for automatic skeleton generation, bridging rigorous data-driven learning with the operational demands of professional animators. By scaling both the training corpus and model sophistication—via semantic part reasoning and explicit user-level controls—the framework sets a new technical standard for automated rigging, and provides new directions for controllable, context-sensitive structured generation in 3D vision and graphics.