  Transferring the pose of a reference avatar to stylized 3D characters of
various shapes is a fundamental task in computer graphics. Existing methods
either require the stylized characters to be rigged, or they use the stylized
character in the desired pose as ground truth at training. We present a
zero-shot approach that requires only the widely available deformed
non-stylized avatars in training, and deforms stylized characters of
significantly different shapes at inference. Classical methods achieve strong
generalization by deforming the mesh at the triangle level, but this requires
labelled correspondences. We leverage the power of local deformation, but
without requiring explicit correspondence labels. We introduce a
semi-supervised shape-understanding module to bypass the need for explicit
correspondences at test time, and an implicit pose deformation module that
deforms individual surface points to match the target pose. Furthermore, to
encourage realistic and accurate deformation of stylized characters, we
introduce an efficient volume-based test-time training procedure. Because it
does not need rigging, nor the deformed stylized character at training time,
our model generalizes to categories with scarce annotation, such as stylized
quadrupeds. Extensive experiments demonstrate the effectiveness of the proposed
method compared to the state-of-the-art approaches trained with comparable or
more supervision. Our project page is available at
https://jiashunwang.github.io/ZPT
