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HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset
Zedong Chu, Feng Xiong, Meiduo Liu, Jinzhi Zhang, Mingqi Shao, Zhaoxu Sun, Di Wang, Mu Xu
Introduces the first large-scale humanoid rigging dataset of 11,434 T-posed meshes and a transformer-based framework fusing skeleton priors with 3D mesh features for automatic rigging.
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, 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 GNNbased 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.
How to read this
- Category
- Dataset + method: automatic humanoid rigging
- Contributions
- HumanRig, presented as the first large-scale humanoid rigging dataset of 11,434 curated T-posed meshes sharing a uniform skeleton topology.
- A data-driven rigging framework with a Prior-Guided Skeleton Estimator (PGSE) that uses 2D skeleton joints to seed a 3D skeleton.
- A Mesh-Skeleton Mutual Attention Network (MSMAN) fusing skeleton and U-shaped point-transformer mesh features for coarse-to-fine joint regression and skinning.
- Context
- Extends neural auto-rigging beyond GNN-based methods such as RigNet, targeting the complex AI-generated meshes that earlier approaches struggled with.Builds on: RigNet: Neural Rigging for Articulated Characters
- Correctness
- Reported to surpass prior methods in quality and versatility, but the uniform skeleton topology and T-pose assumption define the scope, so a reader should keep in mind generalization to non-standard skeletons, non-T-poses, or non-humanoid shapes is outside the dataset's design.
- Clarity
- Reasonably accessible architecture story; a first pass conveys the two-module pipeline, a second pass is needed for the PGSE/MSMAN details.
- How to read it
- Skim the dataset construction and the PGSE-then-MSMAN pipeline first; second pass the attention fusion and skinning estimation if implementing or comparing against RigNet.
Builds on
Built upon by
Nothing yet.
Related work
- RigNet: Neural Rigging for Articulated Characters 2020 / SIGGRAPH
- Automatic Rigging and Animation of 3D Characters 2007 / SIGGRAPH
- Pose and Skeleton-aware Neural IK for Pose and Motion Editing 2023 / SIGGRAPH Asia
- MoRig: Motion-Aware Rigging of Character Meshes from Point Clouds 2022 / SIGGRAPH Asia
Keywords
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