Virtual Reality (VR) applications have revolutionized user experiences 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 virtual world, which enhances interaction and embodiment. However, generating lifelike full-body self-avatar animations remains challenging, particularly in consumer-grade VR systems, where lower-body tracking is often absent. One method to tackle this problem is by providing 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 \textit{YOLOv8} pose estimation. These analyzes show that the studied methods are significantly sensitive to any degradation tested, especially regarding the velocity reconstruction error.

翻译：虚拟现实（VR）应用通过将用户沉浸于交互式3D环境，彻底革新了用户体验。这些环境广泛应用于医疗、教育、建筑等多个领域。VR的一个重要方面是包含代表用户在虚拟世界中的自我化身，这增强了交互与具身感。然而，生成逼真的全身自我化身动画仍具挑战性，尤其是在消费级VR系统中，下肢追踪往往缺失。解决该问题的一种方法是提供包含下肢信息（如从RGB(D)相机估算的完整笛卡尔位置）的外部运动信息源。但此类系统存在多重局限性：两个运动源之间的去同步化以及遮挡是实现此类系统的主要障碍。本文旨在测量以下因素对关节式自我化身全身姿态重建的影响：（1）VR运动特征与估算位置之间的延迟，（2）数据采集速率，（3）遮挡，以及（4）位置估计算法的精度不足。此外，我们利用真值数据和从\textit{YOLOv8}姿态估计中估算的3D笛卡尔坐标分析了运动重建误差。这些分析表明，所研究方法对所测试的任何退化均显著敏感，尤其是速度重建误差方面。