L. Hoyet | Searcharxiv

arXiv2026

REACT: Towards Real-Time Completion of Skeletal Motion Signals

Tracking user motion in interactive and immersive applications is increasingly common (e.g., video games, XR systems, smartphones). Yet, processing and transmitting human motion data remain critical challenges, as current approaches strongly rely on the quality of the tracked motion signals. In general, affordable motion-capture systems may produce unnatural animations in the presence of calibration errors, occlusions, sensor drift, high-frequency noise, or other artifacts. More specifically, we simulate occlusions and high-frequency noise and propose to mitigate their effects with REACT, an interactive online motion-completion framework operating causally, frame by frame, without look-ahead. REACT is an S4-based autoregressive model that coordinates complementary motion estimates under specific reliability-dependent constraints and exposes several interpretable internal signals. We present a system-level contribution consisting of a structured formulation of interactive motion completion together with a coherent instantiation of this formulation. We evaluate REACT against several causal baselines and architectural variants under varying levels of occlusion severity, and report their respective inference latencies. Our results show that REACT achieves a relevant compromise between reconstruction accuracy, update smoothness, and interactive inference. Finally, we characterize how its fusion behaviour changes under missing input observations.

cs.GR↗

arXiv2021

A Survey on Deep Learning for Skeleton-Based Human Animation

Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning and deep reinforcement learning. In this article, we propose a comprehensive survey on the state-of-the-art approaches based on either deep learning or deep reinforcement learning in skeleton-based human character animation. First, we introduce motion data representations, most common human motion datasets and how basic deep models can be enhanced to foster learning of spatial and temporal patterns in motion data. Second, we cover state-of-the-art approaches divided into three large families of applications in human animation pipelines: motion synthesis, character control and motion editing. Finally, we discuss the limitations of the current state-of-the-art methods based on deep learning and/or deep reinforcement learning in skeletal human character animation and possible directions of future research to alleviate current limitations and meet animators' needs.

cs.GR↗