Animator-Centric Skeleton Generation on Objects with Fine-Grained Details
Proposed an animator-centric skeleton generation framework handling complex inputs with control mechanisms, dataset includes 82,633 models.
Key Findings
Methodology
We propose an animator-centric skeleton generation framework combining semantic-aware tokenization and a learnable density control module. By grouping bones into semantically meaningful groups, it enhances robustness to structural complexity and provides a key control mechanism.
Key Results
- On our dataset, the model shows a five- to nine-fold increase in precision and F1 score, particularly excelling in skeleton generation for complex structures.
- Compared to MagicArticulate, our method reduces the J2J distance by 12.2%, demonstrating superior skeleton generation quality.
- Ablation studies indicate that the semantic tokenization strategy reduces J2J distance by 16.3%.
Significance
This research addresses the limitations of current deep learning methods in handling complex structures and providing control. It offers animators higher quality and controllable skeleton generation, significantly improving animation production efficiency and flexibility.
Technical Contribution
Technical contributions include introducing semantic-aware tokenization and a learnable density control module, significantly enhancing skeleton generation accuracy and controllability, with notable advantages over existing methods.
Novelty
This is the first to introduce semantic information into the skeleton generation process, achieving higher generation quality and control through semantic tokenization and density control.
Limitations
- The applicability of semantic tokenization is limited when dealing with non-humanoid and quadruped models.
- Performance under extremely complex structures still needs improvement.
Future Work
Future research directions include extending semantic tokenization strategies to cover more categories and optimizing the model to handle more complex structures.
AI Executive Summary
Skeleton generation is a crucial step in 3D animation production, but existing methods struggle with complex structures and control. We propose an animator-centric skeleton generation framework, combining semantic-aware tokenization and a learnable density control module, capable of generating high-quality skeletons on complex inputs and providing intuitive control handles.
The core of this framework lies in the semantic-aware tokenization strategy, which enhances robustness to structural complexity by grouping bones into semantically meaningful groups. The learnable density control module allows animators to exert soft control over bone density, meeting diverse animation needs.
Experimental results demonstrate that our method excels in skeleton generation for complex structures, significantly outperforming existing methods in precision and F1 score. Future research will focus on extending semantic tokenization strategies and optimizing model performance.
Deep Analysis
Background
Skeleton generation is a core step in 3D animation production, traditionally approached through geometric optimization or data-driven methods. With the advancement of 3D generative models, generating high-quality, complex-structured 3D assets has become easier, but existing methods perform poorly in handling these complex structures.
Core Problem
Existing methods struggle with handling complex structures and providing control, making it difficult to adapt to increasing structural complexity and limiting animators' control over the skeleton generation process.
Innovation
We introduce semantic-aware tokenization and a learnable density control module. Semantic tokenization enhances robustness to complex structures by grouping bones into semantically meaningful groups. The density control module allows animators to exert soft control over bone density.
Methodology
- �� Data preparation: Collected 82,633 models covering various structural complexities.
- �� Semantic-aware tokenization: Group bones by semantic information to enhance robustness.
- �� Density control: Introduced a learnable density control module for soft control over bone density.
Experiments
Experiments were conducted using our collected dataset, comparing multiple baseline models. Evaluation metrics included precision, recall, and F1 score. Ablation studies validated the effectiveness of semantic tokenization and density control.
Results
Our model significantly outperforms baseline models in precision and F1 score, particularly excelling in skeleton generation for complex structures. Ablation studies show that the semantic tokenization strategy significantly reduces J2J distance.
Applications
This method can be directly applied to 3D animation production, especially in scenarios requiring complex structure handling and control. It improves animation production efficiency and flexibility.
Limitations & Outlook
The model's applicability is limited when dealing with non-humanoid and quadruped models. Future research will focus on extending semantic tokenization strategies and optimizing model performance.
Plain Language Accessible to non-experts
Imagine you're building a LEGO model. Traditional methods are like using a single LEGO block to represent a complex structure, while our method breaks the LEGO into different colors and shapes, representing different functional parts. This way, you can not only better understand the overall structure but also adjust each part more flexibly.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to design moves for a character. Old methods are like using a fixed move template, while our method is like giving you a toolbox to freely combine different move modules and create unique moves! Isn't that cool?
Glossary
Auto-regressive Model
A method that generates sequences by predicting the next element in the sequence.
Used for sequence prediction in skeleton generation.
Semantic Tokenization
The process of grouping data based on semantic information.
Used to enhance robustness in skeleton generation.
Density Control
A method to achieve control by adjusting the density of generated elements.
Used to control bone density in skeleton generation.
Skeleton Generation
The process of generating a skeleton for a 3D model.
Used in 3D animation production.
Ablation Study
A study that investigates the impact of removing or replacing parts of a model on overall performance.
Used to validate the effectiveness of semantic tokenization and density control.
Open Questions Unanswered questions from this research
- 1 How to effectively apply semantic tokenization to non-humanoid and quadruped models?
- 2 How to further improve model performance under extremely complex structures?
Applications
Immediate Applications
3D Animation Production
Provides high-quality, controllable skeleton generation, enhancing animation production efficiency.
Long-term Vision
Virtual Reality
Achieve more realistic character animations in virtual reality, enhancing user experience.
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.