Adversary-Guided Motion Retargeting for Skeleton Anonymization
This addresses privacy concerns for users in applications like virtual reality that rely on skeleton data, though it is incremental as it builds on existing motion retargeting techniques.
The paper tackles the problem of removing personally identifiable information (PII) from skeleton-based motion data by introducing a Privacy-centric Deep Motion Retargeting model (PMR) that uses adversarial learning, achieving motion retargeting utility on par with state-of-the-art models while reducing privacy attack performance.
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.