Work

SSR-HMR: Skeleton-Aware Sparse Node-Based Real-Time Human Motion Reconstruction

Year: 2025

Type: article

Abstract: The growing demand for real-time human motion reconstruction in Virtual Reality (VR), Augmented Reality (AR), and the Metaverse requires high accuracy with minimal hardware. This paper presents SSR-HMR, a skeleton-aware, sparse node-based method for full-body motion reconstruction from limited inputs. The approach incorporates a lightweight spatiotemporal graph convolutional module, a torso pose refinement design to mitigate orientation drift, and kinematic tree-based optimization to enhance end-effector positioning accuracy. Smooth motion transitions are achieved via a multi-scale velocity loss. Experiments demonstrate that SSR-HMR achieves high-accuracy reconstruction, with mean joint and end-effector position errors of 1.06 cm and 0.52 cm, respectively, while operating at 267 FPS on a CPU.

Source: Electronics

DOI: 10.3390/electronics14183664

Authors: Linhai Li, Jiayi Lin, Wenhui Zhang

Institutions: Guilin University of Electronic Technology, Guangxi University

Language: English

Topic: Human Motion and Animation

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Natural Science Foundation of Guangxi Province