3D Human Motion Similarity Estimation from Different Perspectives

doi:10.3969/j.issn.1006-2475.2025.07.009

Abstract

Abstract:
Abstract: With the abundance of online fitness and dance instructional videos， students often face difficulties in comparing their movements with those of the instructors due to inconsistencies in angles and scales when filming themselves， which hinders accurate movement similarity comparison. To fix this problem， this paper leverages existing 3D human pose estimation methods and proposes a motion similarity evaluation algorithm for videos filmed from different angles with a monocular camera. For two videos of human actions from different perspectives， this paper first extracts 2D human key points using the YOLOv8pose network， then elevates these to 3D key points using the GraphMLP network. This paper calculates the Euclidean distance matrix based on the two sets of 3D key point sequences and uses the DTW algorithm to identify corresponding frames between the two sets of actions. By adjusting the perspective of corresponding frames’3D key points through rotation and scaling， this paper aligns action sequences from different perspectives. Finally， the cosine similarity of skeletal vectors is used as the similarity evaluation metric. Experiments using mocap animations from different perspectives was conducted， the results demonstrated the effectiveness of the method proposed in this paper.

Key words: Key words: YOLOv8pose, GraphMLP, human pose estimation, dynamic time warping, cosine similarity, different perspectives

CLC Number:

中图分类号：TP391

LI Zihe, WANG Yiding. 3D Human Motion Similarity Estimation from Different Perspectives [J]. Computer and Modernization, 2025, 0(07): 63-68.

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