  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}.
