首个大规模人形角色绑定数据集，实现自动高效绑定
HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset
- 用2D骨骼先验引导3D骨架估计，再融合网格特征进行精细化绑定
- 在11434个T姿势网格上验证，绑定质量优于现有GNN方法
- 适合需要自动化角色绑定的动画制作与游戏开发人员
随着3D生成算法的快速发展，人形角色模型的制作成本大幅降低，但自动绑定领域仍缺乏全面的数据集支持，而绑定是角色动画的关键步骤。为此，我们提出HumanRig，首个专为3D人形角色绑定设计的大规模数据集，包含11,434个经过精心筛选、符合统一骨骼拓扑的T姿势网格。基于该数据集，我们构建了一种创新的、数据驱动的自动绑定框架，克服了现有GNN方法在处理复杂AI生成网格时的局限性。该方法引入先验引导的骨架估计模块（PGSE），利用2D骨骼关节提供初始3D骨架，并结合网格-骨架互注意力网络（MSMAN），融合由U型点变换器提取的网格特征与骨架特征，实现从粗到精的3D关节回归和鲁棒的蒙皮估计。结果在质量和泛化能力上均超越以往方法。本工作不仅填补了绑定研究中的数据空白，也推动了动画行业向更高效、自动化的角色绑定流程发展。
原文摘要 · Abstract (English)
With the rapid evolution of 3D generation algorithms, the cost of producing 3D humanoid character models has plummeted, yet the field is impeded by the lack of a comprehensive dataset for automatic rigging, which is a pivotal step in character animation. Addressing this gap, we present HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging, encompassing 11,434 meticulously curated T-posed meshes adhered to a uniform skeleton topology. Capitalizing on this dataset, we introduce an innovative, data-driven automatic rigging framework, which overcomes the limitations of GNN-based methods in handling complex AI-generated meshes. Our approach integrates a Prior-Guided Skeleton Estimator (PGSE) module, which uses 2D skeleton joints to provide a preliminary 3D skeleton, and a Mesh-Skeleton Mutual Attention Network (MSMAN) that fuses skeleton features with 3D mesh features extracted by a U-shaped point transformer. This enables a coarse-to-fine 3D skeleton joint regression and a robust skinning estimation, surpassing previous methods in quality and versatility. This work not only remedies the dataset deficiency in rigging research but also propels the animation industry towards more efficient and automated character rigging pipelines.
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