Animating a newly designed character using motion capture (mocap) data is a
long standing problem in computer animation. A key consideration is the
skeletal structure that should correspond to the available mocap data, and the
shape deformation in the joint regions, which often requires a tailored,
pose-specific refinement. In this work, we develop a neural technique for
articulating 3D characters using enveloping with a pre-defined skeletal
structure which produces high quality pose dependent deformations. Our
framework learns to rig and skin characters with the same articulation
structure (e.g., bipeds or quadrupeds), and builds the desired skeleton
hierarchy into the network architecture. Furthermore, we propose neural blend
shapes--a set of corrective pose-dependent shapes which improve the deformation
quality in the joint regions in order to address the notorious artifacts
resulting from standard rigging and skinning. Our system estimates neural blend
shapes for input meshes with arbitrary connectivity, as well as weighting
coefficients which are conditioned on the input joint rotations. Unlike recent
deep learning techniques which supervise the network with ground-truth rigging
and skinning parameters, our approach does not assume that the training data
has a specific underlying deformation model. Instead, during training, the
network observes deformed shapes and learns to infer the corresponding rig,
skin and blend shapes using indirect supervision. During inference, we
demonstrate that our network generalizes to unseen characters with arbitrary
mesh connectivity, including unrigged characters built by 3D artists.
Conforming to standard skeletal animation models enables direct plug-and-play
in standard animation software, as well as game engines.

本文通过开发一种基于神经技术的方法，在不假定训练数据的基础形变模型的情况下，使神经网络学习到骨架、皮肤和混合形状，并构建了一个骨骼层次结构的网络架构，利用预定义的骨骼结构使 3D 角色实现高质量的姿态相关形变，其中提出了用于改进关节区域变形质量的纠正姿势依赖形状的方法。