RigNet: Neural Rigging for Articulated Characters
This addresses the labor-intensive task of manual rigging for animators, though it appears incremental as it builds on existing neural approaches for 3D character modeling.
The paper tackles the problem of automatically generating animation rigs for 3D articulated characters, presenting RigNet which predicts skeletons and skin weights from input meshes, achieving better quantitative results than prior methods when compared to animator-created rigs.
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.