ReliaAvatar: A Robust Real-Time Avatar Animator with Integrated Motion Prediction

ReliaAvatar: A Robust Real-Time Avatar Animator with Integrated Motion Prediction

Bo Qian, Zhenhuan Wei, Jiashuo Li, Xing Wei

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 3124-3132. https://doi.org/10.24963/ijcai.2024/346

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 ReliaAvatar, a real-time, reliable 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 ReliaAvatar's superior performance in both standard and low data-quality conditions. The code is available at https://github.com/MIV-XJTU/ReliaAvatar.
Keywords:
Humans and AI: HAI: Applications
Humans and AI: HAI: Human-computer interaction
Humans and AI: HAI: Personalization and user modeling
Robotics: ROB: Human robot interaction