Body-Scale-Invariant Motion Embedding for Motion Similarity
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Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Accurate measurement of motion similarity is crucial for applications in healthcare, rehabilitation, sports analysis, and human- computer interaction. However, existing Human Pose Estimation (HPE) approaches often conflate motion dynamics with anatomical variations, leading to body-scale-dependent similarity assessments. We propose a framework for learning bodyscale- invariant motion embeddings directly from RGB videos. Leveraging diverse 3D character animations with varied skeletal proportions, we generate standardized motion data and train the SAME model to capture temporal dynamics independent of body size. Our approach enables robust cross-character motion similarity evaluation. Experimental results show that the method effectively decouples kinematic patterns from structural differences, outperforming scale-sensitive baselines. Key contributions include: (1) a scalable motion data processing pipeline; (2) a learning-based body-scale-invariant embedding method; and (3) validation of motion similarity assessment independent of anatomy.
Description
CCS Concepts: Computing methodologies → Computer graphics; Motion processing; Neural networks; Learning latent representations
@inproceedings{10.2312:pg.20251311,
booktitle = {Pacific Graphics Conference Papers, Posters, and Demos},
editor = {Christie, Marc and Han, Ping-Hsuan and Lin, Shih-Syun and Pietroni, Nico and Schneider, Teseo and Tsai, Hsin-Ruey and Wang, Yu-Shuen and Zhang, Eugene},
title = {{Body-Scale-Invariant Motion Embedding for Motion Similarity}},
author = {Du, Xian and Quan, Chuyan and Yu, Ri},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-295-0},
DOI = {10.2312/pg.20251311}
}