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

arXiv: https://arxiv.org/abs/2407.02129 · HTML: https://mlsanity.com/paper/2407.02129/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [Dynamic Integration of Task-Specific Adapters for Class Incremental Learning](https://mlsanity.com/paper/2409.14983/) (2024)
- [From Sparse Signal to Smooth Motion: Real-Time Motion Generation with Rolling Prediction Models](https://mlsanity.com/paper/2504.05265/) (2025)
- [KineST: A Kinematics-guided Spatiotemporal State Space Model for Human Motion Tracking from Sparse Signals](https://mlsanity.com/paper/2512.16791/) (2025)

## Abstract (arXiv)

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 textitReliaAvatar, 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 Relia-Avatar's superior performance in both standard and low data-quality conditions. The code is available at urlhttps://github.com/MIV-XJTU/ReliaAvatar.