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.

翻译：随着三维生成算法的快速发展，三维人形角色模型的制作成本已大幅降低，但该领域因缺乏用于自动绑定的综合性数据集而受到阻碍，而绑定是角色动画中的关键步骤。为填补这一空白，我们提出了HumanRig——首个专门为三维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、遵循统一骨骼拓扑结构的T姿态网格。基于此数据集，我们提出了一种创新的数据驱动自动绑定框架，克服了基于图神经网络的方法在处理复杂AI生成网格时的局限性。我们的方法集成了一个先验引导骨骼估计器模块，该模块利用二维骨骼关节点提供初步的三维骨骼；以及一个网格-骨骼互注意力网络，该网络将骨骼特征与通过U形点Transformer提取的三维网格特征相融合。这使得从粗到精的三维骨骼关节回归以及鲁棒的蒙皮估计成为可能，在质量和泛化能力上均超越了先前方法。本工作不仅弥补了绑定研究中的数据缺失，也推动了动画产业向更高效、自动化的角色绑定流程发展。