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Estimating 3D full-body pose from sparse sensor data is a pivotal techniqueemployed for the reconstruction of realistic human motions in Augmented Realityand Virtual Reality. However, translating sparse sensor signals intocomprehensive human motion remains a challenge since the sparsely distributedsensors in common VR systems fail to capture the motion of full human body. Inthis paper, we use well-designed Body Pose Graph (BPG) to represent the humanbody and translate the challenge into a prediction problem of graph missingnodes. Then, we propose a novel full-body motion reconstruction framework basedon BPG. To establish BPG, nodes are initially endowed with features extractedfrom sparse sensor signals. Features from identifiable joint nodes acrossdiverse sensors are amalgamated and processed from both temporal and spatialperspectives. Temporal dynamics are captured using the Temporal PyramidStructure, while spatial relations in joint movements inform the spatialattributes. The resultant features serve as the foundational elements of theBPG nodes. To further refine the BPG, node features are updated through a graphneural network that incorporates edge reflecting varying joint relations. Ourmethod's effectiveness is evidenced by the attained state-of-the-artperformance, particularly in lower body motion, outperforming other baselinemethods. Additionally, an ablation study validates the efficacy of each modulein our proposed framework.

Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective