Hybrid Retrieval-Regression for Motion-Driven Loose-Fitting Garment Animation

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dc.contributor.authorLee, Myeongjinen_US
dc.contributor.authorLibao, Emmanuel Ianen_US
dc.contributor.authorLee, Sung-Heeen_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.accessioned2025-10-07T06:02:30Z
dc.date.available2025-10-07T06:02:30Z
dc.date.issued2025
dc.description.abstractWe present a hybrid retrieval-regression framework for motion-driven garment animation leveraging a shared discrete codebook. Our method targets the challenge of animating loose-fitting garments, whose dynamic behaviors exhibit high variability and less direct correlation with body motion-making them difficult to handle with conventional example-based approaches that assume tightly coupled motion-garment relationships. To address this, we project both motion and garment animation clips into a shared discrete codebook via Gumbel-Softmax-based quantization, allowing them to be aligned in a semantically consistent space where cross-retrieval can be performed using simple distance metrics. During inference, we adaptively switch between retrieval and regression based on the confidence derived from the codebook probability distribution, allowing the system to remain robust in the presence of ambiguous or unseen motions. We leverage a pre-trained mesh autoencoder to obtain garment latents that preserve local geometric structure, enabling smoother transitions and more geometrically consistent interpolation between retrieved and regressed animation segments efficiently. Experimental results demonstrate that our approach improves the accuracy and plausibility of garment animation for complex garments under diverse motion inputs, while maintaining robustness to unseen scenarios and achieving low simulation error for high-quality garment animation.en_US
dc.description.sectionheadersDigital Clothing
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251259
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251259
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251259
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 → Animation; Learning latent representations; Discrete space search
dc.subjectComputing methodologies → Animation
dc.subjectLearning latent representations
dc.subjectDiscrete space search
dc.titleHybrid Retrieval-Regression for Motion-Driven Loose-Fitting Garment Animationen_US
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