RigNet: Neural Rigging for Articulated Characters

SIGGRAPH 2020

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 fnally, we evaluate the impact of various algorithm choices on our output rigs.


Paper

RigNet.pdf, 11.6MB

Video

Source Code & Data

Github code: https://github.com/zhan-xu/RigNet

Dataset: ModelsResource-RigNetv1

Citation

If you use our dataset or code, please cite the following papers.
  @InProceedings{AnimSkelVolNet,
    title={Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets},
    author={Zhan Xu and Yang Zhou and Evangelos Kalogerakis and Karan Singh},
    booktitle={2019 International Conference on 3D Vision (3DV)},
    year={2019}
  }
  @article{RigNet,
    title={RigNet: Neural Rigging for Articulated Characters},
    author={Zhan Xu and Yang Zhou and Evangelos Kalogerakis and Chris Landreth and Karan Singh},
    journal={ACM Trans. on Graphics},
    year={2020},
    volume={39}
  }

Acknowledgements

This research is partially funded by NSF (EAGER-1942069) and NSERC. Our experiments were performed in the UMass GPU cluster obtained under the Collaborative Fund managed by the Massachusetts Technology Collaborative.