Efficiently estimating the full-body pose with minimal wearable devices presents a worthwhile research direction. Despite significant advancements in this field, most current research neglects to explore full-body avatar estimation under low-quality signal conditions, which is prevalent in practical usage. To bridge this gap, we summarize three scenarios that may be encountered in real-world applications: standard scenario, instantaneous data-loss scenario, and prolonged data-loss scenario, and propose a new evaluation benchmark. The solution we propose to address data-loss scenarios is integrating the full-body avatar pose estimation problem with motion prediction. Specifically, we present \textit{ReliaAvatar}, a real-time, \textbf{relia}ble \textbf{avatar} animator equipped with predictive modeling capabilities employing a dual-path architecture. ReliaAvatar operates effectively, with an impressive performance rate of 109 frames per second (fps). Extensive comparative evaluations on widely recognized benchmark datasets demonstrate Relia\-Avatar's superior performance in both standard and low data-quality conditions. The code is available at \url{https://github.com/MIV-XJTU/ReliaAvatar}.

翻译：利用最少的可穿戴设备高效估计全身姿态是一个值得研究的方向。尽管该领域已取得显著进展，但当前大多数研究忽略了在低质量信号条件下探索全身虚拟角色估计，而这在实际应用中普遍存在。为填补这一空白，我们总结了现实应用中可能遇到的三种场景：标准场景、瞬时数据丢失场景和长时间数据丢失场景，并提出了一种新的评估基准。我们针对数据丢失场景提出的解决方案是将全身虚拟角色姿态估计问题与运动预测相结合。具体而言，我们提出了\textit{ReliaAvatar}——一种实时、可靠（\textbf{relia}ble）且具备预测建模能力的虚拟角色（\textbf{avatar}）动画生成器，其采用双路径架构。ReliaAvatar运行高效，性能表现令人印象深刻，达到每秒109帧（fps）。在广泛认可的基准数据集上进行的大量对比评估表明，ReliaAvatar在标准条件和低数据质量条件下均表现出优越的性能。代码发布于\url{https://github.com/MIV-XJTU/ReliaAvatar}。