Privacy-Centric Deep Motion Retargeting for Anonymization of Skeleton-Based Motion Visualization

Abstract

Capturing and visualizing motion using skeleton-based techniques is a key aspect of computer vision, particularly in virtual reality (VR) settings. Its popularity has surged, driven by the simplicity of obtaining skeleton data and the growing appetite for virtual interaction. Although this skeleton data appears to be non-identifiable, it can be exploited to derive personally identifiable information (PII), posing a risk of inadvertent privacy breaches. In this paper, we explore the application of motion retargeting and its ability to mitigate privacy leakages. Motion retargeting can effectively transfer the motion from an initial user onto a dummy skeleton with the purpose of hiding PII. We propose a Privacy-centric Deep Motion Retargeting model (PMR), which mitigates the PII through adversarial learning. In our evaluation, our proposed model achieves motion retargeting performance on par with the current state-of-the-art models. More importantly, it effectively prevents the attackers from identifying the initial user.