arXiv Computer Vision September 17, 2026 By Yuepeng Chen, Jiehong Shi, Kaili Zheng, Boyi Zhang, Chenyi Guo, Ji Wu, Xiangling Fu

Pose2Muscle: Structured Spatio-Temporal Decoding for Discrete Muscle Activity Estimation from Human Pose

Read the original on arXiv Computer Vision →

Pose2Muscle is a pose-driven framework that estimates discrete muscle activity states without requiring surface electromyography (sEMG) during inference. It reformulates muscle estimation as a structured prediction problem, using multi-scale spatio-temporal attention and a directed acyclic graph-based decoder to capture motion patterns and maintain multiple candidate hypotheses. The authors introduce the PoseEMG-43 dataset, comprising 2,992 movement instances from 43 daily-life actions performed by 14 participants, and demonstrate that Pose2Muscle outperforms baseline methods with high accuracy and correlation metrics.

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