Motion retargeting is a fundamental problem in computer graphics and computer
vision. Existing approaches usually have many strict requirements, such as the
source-target skeletons needing to have the same number of joints or share the
same topology. To tackle this problem, we note that skeletons with different
structure may have some common body parts despite the differences in joint
numbers. Following this observation, we propose a novel, flexible motion
retargeting framework. The key idea of our method is to regard the body part as
the basic retargeting unit rather than directly retargeting the whole body
motion. To enhance the spatial modeling capability of the motion encoder, we
introduce a pose-aware attention network (PAN) in the motion encoding phase.
The PAN is pose-aware since it can dynamically predict the joint weights within
each body part based on the input pose, and then construct a shared latent
space for each body part by feature pooling. Extensive experiments show that
our approach can generate better motion retargeting results both qualitatively
and quantitatively than state-of-the-art methods. Moreover, we also show that
our framework can generate reasonable results even for a more challenging
retargeting scenario, like retargeting between bipedal and quadrupedal
skeletons because of the body part retargeting strategy and PAN. Our code is
publicly available.

提出了一种基于身体部位的柔性运动重定向框架，其在运动编码阶段引入了姿势感知注意网络（PAN），并使用特征池化构建每个身体部位的共享潜在空间，通过在运动解码阶段应用该框架，可以在更具挑战性的重定向情景中获得合理的结果。

基于姿态感知的身体部位灵活运动重定向注意力网络

Pose-aware Attention Network for Flexible Motion Retargeting by Body  Part

Humans constantly contact objects to move and perform tasks. Thus, detecting
human-object contact is important for building human-centered artificial
intelligence. However, there exists no robust method to detect contact between
the body and the scene from an image, and there exists no dataset to learn such
a detector. We fill this gap with HOT ("Human-Object conTact"), a new dataset
of human-object contacts for images. To build HOT, we use two data sources: (1)
We use the PROX dataset of 3D human meshes moving in 3D scenes, and
automatically annotate 2D image areas for contact via 3D mesh proximity and
projection. (2) We use the V-COCO, HAKE and Watch-n-Patch datasets, and ask
trained annotators to draw polygons for the 2D image areas where contact takes
place. We also annotate the involved body part of the human body. We use our
HOT dataset to train a new contact detector, which takes a single color image
as input, and outputs 2D contact heatmaps as well as the body-part labels that
are in contact. This is a new and challenging task that extends current
foot-ground or hand-object contact detectors to the full generality of the
whole body. The detector uses a part-attention branch to guide contact
estimation through the context of the surrounding body parts and scene. We
evaluate our detector extensively, and quantitative results show that our model
outperforms baselines, and that all components contribute to better
performance. Results on images from an online repository show reasonable
detections and generalizability.

本文介绍了一种用于检测人与周围物体接触的新数据集 HOT，并使用该数据集训练了一个新的接触检测器，它可以输出 2D 接触热图以及处于接触状态的身体部位标签。该检测器采用了部分注意分支来通过周围身体部分和场景的上下文指导接触估计，并且定量的结果显示，该模型的性能优于基线，并且所有组件都对提高性能有贡献。