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。

神经姿势表示学习用于生成和转移非刚性物体姿势

Neural Pose Representation Learning for Generating and Transferring  Non-Rigid Object Poses

Supervised deep learning with pixel-wise training labels has great successes
on multi-person part segmentation. However, data labeling at pixel-level is
very expensive. To solve the problem, people have been exploring to use
synthetic data to avoid the data labeling. Although it is easy to generate
labels for synthetic data, the results are much worse compared to those using
real data and manual labeling. The degradation of the performance is mainly due
to the domain gap, i.e., the discrepancy of the pixel value statistics between
real and synthetic data. In this paper, we observe that real and synthetic
humans both have a skeleton (pose) representation. We found that the skeletons
can effectively bridge the synthetic and real domains during the training. Our
proposed approach takes advantage of the rich and realistic variations of the
real data and the easily obtainable labels of the synthetic data to learn
multi-person part segmentation on real images without any human-annotated
labels. Through experiments, we show that without any human labeling, our
method performs comparably to several state-of-the-art approaches which require
human labeling on Pascal-Person-Parts and COCO-DensePose datasets. On the other
hand, if part labels are also available in the real-images during training, our
method outperforms the supervised state-of-the-art methods by a large margin.
We further demonstrate the generalizability of our method on predicting novel
keypoints in real images where no real data labels are available for the novel
keypoints detection. Code and pre-trained models are available at
this https URL

本文提出了一种无需使用人工注释标签，仅仅利用丰富真实数据和易获得合成数据标签的方法，通过姿态表示有效地建立了合成和真实域之间的桥梁，以学习在真实图像上进行多人身体部位分割的方法，并显示该方法表现出与需人工标注标签的现有方法可媲美的效果。