3D pose transfer is one of the most challenging 3D generation tasks. It aims
to transfer the pose of a source mesh to a target mesh and keep the identity
(e.g., body shape) of the target mesh. Some previous works require key point
annotations to build reliable correspondence between the source and target
meshes, while other methods do not consider any shape correspondence between
sources and targets, which leads to limited generation quality. In this work,
we propose a correspondence-refinement network to achieve the 3D pose transfer
for both human and animal meshes. The correspondence between source and target
meshes is first established by solving an optimal transport problem. Then, we
warp the source mesh according to the dense correspondence and obtain a coarse
warped mesh. The warped mesh will be better refined with our proposed Elastic
Instance Normalization, which is a conditional normalization layer and can help
to generate high-quality meshes. Extensive experimental results show that the
proposed architecture can effectively transfer the poses from source to target
meshes and produce better results with satisfied visual performance than
state-of-the-art methods.

该论文提出了一种对人类和动物网格控制姿势的方法，该方法通过优化传输问题建立源网格和目标网格之间的对应关系，并使用弹性实例归一化的条件归一化层来提高生成网格的质量。