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arXiv · 2005.00559

RigNet: Neural Rigging for Articulated Characters

Zhan Xu·Yang Zhou·Evangelos Kalogerakis·Chris Landreth·Karan Singh
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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.

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Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Chris Landreth, Karan Singh. 2020-05-01. RigNet: Neural Rigging for Articulated Characters. https://arxiv.org/abs/2005.00559

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