AI RESEARCH
Deep Learning for Virtual Reality User Identification: A Benchmark
arXiv CS.CV
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ArXi:2604.16341v1 Announce Type: cross Virtual Reality (VR) applications require robust user identification systems to ensure secure access to equipment and protect worker identities. Motion tracking data from VR headsets and controllers has emerged as a powerful behavioral biometric, with recent studies nstrating identification accuracies exceeding 94% across a large user base. However, the application of modern deep learning architectures, particularly State Space Models (SSM), to VR scenarios remains largely unexplored.