Low-Latency Online Estimation of Human Upper-Limb Pose and Kinematics from a Single 360° Camera

We present a fully online framework for streaming human upper-limb kinematics estimation from a single 360° camera. Incoming frames are processed sequentially through vertical-boundary-aware tracking, pseudo-perspective rendering, and Neural Localizer Fields to estimate a sparse set of 3D anatomical landmarks in real time. These landmarks are mapped to an OpenSim-compatible biomechanical model, with joint angles computed on the fly via an online inverse kinematics solver. The system achieves end-to-end latencies as low as 22.9 ms on a high-performance setup. Evaluated in a single- participant scenario involving an initial T-pose calibration and repeated object displacement toward the camera, it demonstrates robust performance under moderate self-occlusion and spherical distortion. While tested in a constrained setting, its modular, real- time design makes it a promising candidate for human–robot interaction and other motion analysis applications, enabling minimal, markerless, and anatomically interpretable upper-limb tracking from omnidirectional vision.