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.

该研究提出 RigNet：一种基于深度学习的自动化动画角色建模软件，通过输入 3D 模型可以精确预测骨骼结构和表面权重，并且可以比其他算法更好地模拟动态效果。