RaStream: Edge-Deployable Streaming Human Mesh Recovery from mmWave Radar

Abstract

Millimeter-wave (mmWave) radar provides a privacy-preserving sensing modality for human motion analysis, yet continuous SMPL-X recovery on edge devices remains challenging because accurate reconstruction benefits from dense volumetric radar observations that preserve weak articulated reflections, while repeatedly processing such high-dimensional data over time incurs substantial computational cost. Temporal context is also necessary to resolve frame-local ambiguity, but SMPL-X attributes evolve at different rates, with body morphology remaining relatively stable and articulated pose, root orientation, and global translation changing rapidly. We present RaStream, a causal streaming framework that separates rich volumetric spatial perception from lightweight temporal reasoning. For each radar observation, a localization-conditioned spatial encoder exploits global scene context to estimate a coarse body location and focuses detailed reconstruction on a center-guided region of interest while preserving native 3D radar structure. Global and local features are then fused into a compact representation, allowing volumetric evidence to be extracted once per observation and temporal history to be propagated through compact representations and fixed-size persistent states. RaStream further maintains a slow morphology state for shape-related information and a fast motion state for pose, orientation, and translation. On the 661K-frame M4Human benchmark, RaStream reduces MVE from 90.90 mm for RT-Mesh to 84.27 mm with spatial-only inference and to 72.05 mm with causal temporal refinement. The Base configuration requires 26.93 ms per FP32 invocation on a Jetson Orin Nano. These results demonstrate practical edge-streaming radar mesh recovery through one-time volumetric evidence extraction and compact causal temporal propagation.