构建23万件3D模型的自动绑定数据集，提升复杂物体动画生成效果
Anymate: A Dataset and Baselines for Learning 3D Object Rigging
- 分三阶段学习预测关节、连接关系和权重，实现端到端自动绑定
- 在23万数据上训练，性能显著优于现有方法，为后续研究提供基准
- 适合3D动画自动化、游戏建模等领域的研究人员和工程师
绑定与蒙皮是创建逼真3D动画的关键步骤，通常需要大量专业知识和手工操作。传统自动化方法依赖几何启发式规则，对复杂形状对象表现不佳。近期基于数据的方法虽具潜力，但受限于训练数据规模。本文提出Anymate数据集，包含23万件3D资产及其专家制作的绑定与蒙皮信息，规模是现有数据集的70倍。基于该数据集，我们设计了一个分三阶段的学习型自动绑定框架，分别预测关节、连接关系和蒙皮权重。系统性地测试了多种架构作为各模块基线，并在数据集上进行全面评估，结果表明所提模型显著优于现有方法，为未来自动化绑定研究提供了可比较的基础。代码与数据集详见https://anymate3d.github.io/。
原文摘要 · Abstract (English)
Rigging and skinning are essential steps to create realistic 3D animations, often requiring significant expertise and manual effort. Traditional attempts at automating these processes rely heavily on geometric heuristics and often struggle with objects of complex geometry. Recent data-driven approaches show potential for better generality, but are often constrained by limited training data. We present the Anymate Dataset, a large-scale dataset of 230K 3D assets paired with expert-crafted rigging and skinning information -- 70 times larger than existing datasets. Using this dataset, we propose a learning-based auto-rigging framework with three sequential modules for joint, connectivity, and skinning weight prediction. We systematically design and experiment with various architectures as baselines for each module and conduct comprehensive evaluations on our dataset to compare their performance. Our models significantly outperform existing methods, providing a foundation for comparing future methods in automated rigging and skinning. Code and dataset can be found at https://anymate3d.github.io/.
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