Adversary-Guided Motion Retargeting for Skeleton Anonymization

TL;DR

This paper introduces a novel adversary-guided motion retargeting technique that enhances skeleton anonymization in VR by effectively removing personally identifiable information while maintaining motion fidelity.

Contribution

It proposes a Privacy-centric Deep Motion Retargeting (PMR) model that uses adversarial learning to improve skeleton anonymization without sacrificing motion quality.

Findings

PMR achieves state-of-the-art motion retargeting performance.

PMR significantly reduces privacy attack success rates.

The method maintains high motion utility while enhancing privacy protection.

Abstract

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.