Parametric models of humans, faces, hands and animals have been widely used
for a range of tasks such as image-based reconstruction, shape correspondence
estimation, and animation. Their key strength is the ability to factor surface
variations into shape and pose dependent components. Learning such models
requires lots of expert knowledge and hand-defined object-specific constraints,
making the learning approach unscalable to novel objects. In this paper, we
present a simple yet effective approach to learn disentangled shape and pose
representations in an unsupervised setting. We use a combination of
self-consistency and cross-consistency constraints to learn pose and shape
space from registered meshes. We additionally incorporate as-rigid-as-possible
deformation(ARAP) into the training loop to avoid degenerate solutions. We
demonstrate the usefulness of learned representations through a number of tasks
including pose transfer and shape retrieval. The experiments on datasets of 3D
humans, faces, hands and animals demonstrate the generality of our approach.
Code is made available at
this https URL

本文提出了一种简单但有效的无监督学习方法，结合自一致性和交叉一致性约束，并将尽可能刚性形变集成到训练循环中，以学习体态模型中的形状和姿态空间，并演示了学习表示的实用性，包括姿势转移和形状检索，实验证明了该方法的通用性。