Skeleton-based motion visualization is a rising field in computer vision,
especially in the case of virtual reality (VR). With further advancements in
human-pose estimation and skeleton extracting sensors, more and more
applications that utilize skeleton data have come about. These skeletons may
appear to be anonymous but they contain embedded personally identifiable
information (PII). In this paper we present a new anonymization technique that
is based on motion retargeting, utilizing adversary classifiers to further
remove PII embedded in the skeleton. Motion retargeting is effective in
anonymization as it transfers the movement of the user onto the a dummy
skeleton. In doing so, any PII linked to the skeleton will be based on the
dummy skeleton instead of the user we are protecting. We propose a
Privacy-centric Deep Motion Retargeting model (PMR) which aims to further clear
the retargeted skeleton of PII through adversarial learning. In our
experiments, PMR achieves motion retargeting utility performance on par with
state of the art models while also reducing the performance of privacy attacks.

基于运动重定向的隐私保护深度动作重定向模型（PMR）通过对抗学习，帮助实现与最先进模型相当的运动重定向效果，并降低隐私攻击的成功率。