Body-Scale-Invariant Motion Embedding for Motion Similarity

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dc.contributor.authorDu, Xianen_US
dc.contributor.authorQuan, Chuyanen_US
dc.contributor.authorYu, Rien_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:05:25Z
dc.date.available2025-10-07T06:05:25Z
dc.date.issued2025
dc.description.abstractAccurate 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.en_US
dc.description.sectionheadersPosters and Demos
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251311
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251311
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251311
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Computer graphics; Motion processing; Neural networks; Learning latent representations
dc.subjectComputing methodologies → Computer graphics
dc.subjectMotion processing
dc.subjectNeural networks
dc.subjectLearning latent representations
dc.titleBody-Scale-Invariant Motion Embedding for Motion Similarityen_US
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