Impact of 3D Cartesian Positions and Occlusion on Self-Avatar Full-Body Animation in Virtual Reality

Virtual Reality (VR) applications have revolutionized user expe- riences by immersing individuals in interactive 3D environments. These environments find applications in numerous fields, including healthcare, education, or architecture. A significant aspect of VR is the inclusion of self-avatars, representing users within the vir- tual world, which enhances interaction and embodiment. However, generating lifelike full-body self-avatar animations remains challeng- ing, particularly in consumer-grade VR systems, where lower-body tracking is often absent. One method to tackle this problem is by pro- viding an external source of motion information that includes lower body information such as full Cartesian positions estimated from RGB(D) cameras. Nevertheless, the limitations of these systems are multiples: the desynchronization between the two motion sources and occlusions are examples of significant issues that hinder the implementations of such systems. In this paper, we aim to measure the impact on the reconstruction of the articulated self-avatar’s full- body pose of (1) the latency between the VR motion features and estimated positions, (2) the data acquisition rate, (3) occlusions, and (4) the inaccuracy of the position estimation algorithm. In addition, we analyze the motion reconstruction errors using ground truth and 3D Cartesian coordinates estimated from YOLOv8 pose estimation. These analyzes show that the studied methods are significantly sen- sitive to any degradation tested, especially regarding the velocity reconstruction error.