让下一篇重要论文，不再错过。

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.