The main challenges of 3D pose transfer are: 1) Lack of paired training data
with different characters performing the same pose; 2) Disentangling pose and
shape information from the target mesh; 3) Difficulty in applying to meshes
with different topologies. We thus propose a novel weakly-supervised
keypoint-based framework to overcome these difficulties. Specifically, we use a
topology-agnostic keypoint detector with inverse kinematics to compute
transformations between the source and target meshes. Our method only requires
supervision on the keypoints, can be applied to meshes with different
topologies and is shape-invariant for the target which allows extraction of
pose-only information from the target meshes without transferring shape
information. We further design a cycle reconstruction to perform
self-supervised pose transfer without the need for ground truth deformed mesh
with the same pose and shape as the target and source, respectively. We
evaluate our approach on benchmark human and animal datasets, where we achieve
superior performance compared to the state-of-the-art unsupervised approaches
and even comparable performance with the fully supervised approaches. We test
on the more challenging Mixamo dataset to verify our approach's ability in
handling meshes with different topologies and complex clothes. Cross-dataset
evaluation further shows the strong generalization ability of our approach.

三维姿势转换的主要挑战是：1）没有不同角色执行相同姿势的配对训练数据；2）从目标网格中分离姿势和形状信息；3）难以应用于具有不同拓扑结构的网格。因此，我们提出了一种新颖的弱监督基于关键点的框架以克服这些困难。具体而言，我们使用一种与拓扑无关的关键点检测器和逆向运动学来计算源网格与目标网格之间的变换。我们的方法只需要对关键点进行监督，可以应用于具有不同拓扑结构的网格，并且对于目标来说是形状不变的，可以从目标网格中提取仅限于姿势的信息而不传递形状信息。我们进一步设计了一个循环重建来执行自监督姿势转换，而不需要具有与目标和源相同姿势和形状的地面实况变形网格。我们在基准人类和动物数据集上评估了我们的方法，在那里与最先进的无监督方法相比达到了卓越的性能，甚至与完全监督的方法相比具有可比性的性能。我们在更具挑战性的 Mixamo 数据集上进行测试，以验证我们的方法处理具有不同拓扑结构和复杂服装能力。跨数据集评估进一步展示了我们方法的强大泛化能力。