RigNet: Neural Rigging for Articulated Characters
arXiv:2005.00559
Abstract
We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet predicts a skeleton that matches the animator expectations in joint placement and topology. It also estimates surface skin weights based on the predicted skeleton. Our method is based on a deep architecture that directly operates on the mesh representation without making assumptions on shape class and structure. The architecture is trained on a large and diverse collection of rigged models, including their mesh, skeletons and corresponding skin weights. Our evaluation is three-fold: we show better results than prior art when quantitatively compared to animator rigs; qualitatively we show that our rigs can be expressively posed and animated at multiple levels of detail; and finally, we evaluate the impact of various algorithm choices on our output rigs.
SIGGRAPH 2020. Project page https://zhan-xu.github.io/rig-net/
References in corpus (10)
- Semi-Supervised Classification with Graph Convolutional Networks
- Geometric deep learning: going beyond Euclidean data
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- Deep Convolutional Networks on Graph-Structured Data
- MeshCNN: A Network with an Edge
- Interaction Networks for Learning about Objects, Relations and Physics
- Geodesics in Heat
- DeepWarp: DNN-based Nonlinear Deformation
- NASA: Neural Articulated Shape Approximation
- Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences