We propose a novel method for learning representations of poses for 3D
deformable objects, which specializes in 1) disentangling pose information from
the object's identity, 2) facilitating the learning of pose variations, and 3)
transferring pose information to other object identities. Based on these
properties, our method enables the generation of 3D deformable objects with
diversity in both identities and poses, using variations of a single object. It
does not require explicit shape parameterization such as skeletons or joints,
point-level or shape-level correspondence supervision, or variations of the
target object for pose transfer. To achieve pose disentanglement, compactness
for generative models, and transferability, we first design the pose extractor
to represent the pose as a keypoint-based hybrid representation and the pose
applier to learn an implicit deformation field. To better distill pose
information from the object's geometry, we propose the implicit pose applier to
output an intrinsic mesh property, the face Jacobian. Once the extracted pose
information is transferred to the target object, the pose applier is fine-tuned
in a self-supervised manner to better describe the target object's shapes with
pose variations. The extracted poses are also used to train a cascaded
diffusion model to enable the generation of novel poses. Our experiments with
the DeformThings4D and Human datasets demonstrate state-of-the-art performance
in pose transfer and the ability to generate diverse deformed shapes with
various objects and poses.

我们提出了一种新的学习三维可变形物体姿态表示的方法，该方法能够在物体的不同标识和姿态之间进行解耦，并通过单一对象的变化实现多样化的姿态。通过设计姿态提取器和姿态应用器来实现姿态解耦、生成模型的紧凑性和可转移性，并利用隐式姿态应用器来提取姿态信息和实现目标对象的形状变化。通过实验验证了姿态转移和生成多样性变形形状的最新性能。