HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset

TL;DR

HumanRig introduces a large-scale dataset for 3D humanoid rigging and a novel data-driven framework that improves automatic rigging accuracy and robustness, advancing the efficiency of character animation pipelines.

Contribution

The paper provides the first large-scale humanoid rigging dataset and a new automatic rigging method that outperforms existing GNN-based approaches in handling complex AI-generated meshes.

Findings

The dataset contains 11,434 T-posed meshes with a uniform skeleton topology.

The proposed framework achieves superior rigging quality compared to previous methods.

The approach enhances automation and robustness in character rigging processes.

Abstract

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.