HeterSkinNet: A Heterogeneous Network for Skin Weights Prediction
Xiaoyu Pan, Jiancong Huang, Jiaming Mai, He Wang, Honglin Li, Tongkui, Su, Wenjun Wang, Xiaogang Jin

TL;DR
HeterSkinNet is a novel graph-based method that automates character rigging by learning relationships between mesh vertices and skeleton bones, accommodating complex topologies and outperforming existing methods.
Contribution
It introduces a new heterogeneous graph convolution operator and distance measure, enabling robust and accurate automatic skin weight prediction for diverse character models.
Findings
Outperforms state-of-the-art methods in rigging accuracy
Robust to arbitrary mesh and skeleton topologies
Significantly improves automation and productivity in character rigging
Abstract
Character rigging is universally needed in computer graphics but notoriously laborious. We present a new method, HeterSkinNet, aiming to fully automate such processes and significantly boost productivity. Given a character mesh and skeleton as input, our method builds a heterogeneous graph that treats the mesh vertices and the skeletal bones as nodes of different types and uses graph convolutions to learn their relationships. To tackle the graph heterogeneity, we propose a new graph network convolution operator that transfers information between heterogeneous nodes. The convolution is based on a new distance HollowDist that quantifies the relations between mesh vertices and bones. We show that HeterSkinNet is robust for production characters by providing the ability to incorporate meshes and skeletons with arbitrary topologies and morphologies (e.g., out-of-body bones, disconnected mesh…
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Taxonomy
TopicsHuman Pose and Action Recognition · 3D Shape Modeling and Analysis · Human Motion and Animation
