LUCAS: Layered Universal Codec Avatars
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915202786656256 |
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| author | Liu, Di Deng, Teng Nam, Giljoo Rong, Yu Pidhorskyi, Stanislav Li, Junxuan Saragih, Jason Metaxas, Dimitris N. Cao, Chen |
| author_facet | Liu, Di Deng, Teng Nam, Giljoo Rong, Yu Pidhorskyi, Stanislav Li, Junxuan Saragih, Jason Metaxas, Dimitris N. Cao, Chen |
| contents | Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during expressions and head movements. We present LUCAS, a novel Universal Prior Model (UPM) for codec avatar modeling that disentangles face and hair through a layered representation. Unlike previous UPMs that treat hair as an integral part of the head, our approach separates the modeling of the hairless head and hair into distinct branches. LUCAS is the first to introduce a mesh-based UPM, facilitating real-time rendering on devices. Our layered representation also improves the anchor geometry for precise and visually appealing Gaussian renderings. Experimental results indicate that LUCAS outperforms existing single-mesh and Gaussian-based avatar models in both quantitative and qualitative assessments, including evaluations on held-out subjects in zero-shot driving scenarios. LUCAS demonstrates superior dynamic performance in managing head pose changes, expression transfer, and hairstyle variations, thereby advancing the state-of-the-art in 3D head avatar reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19739 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LUCAS: Layered Universal Codec Avatars Liu, Di Deng, Teng Nam, Giljoo Rong, Yu Pidhorskyi, Stanislav Li, Junxuan Saragih, Jason Metaxas, Dimitris N. Cao, Chen Computer Vision and Pattern Recognition Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during expressions and head movements. We present LUCAS, a novel Universal Prior Model (UPM) for codec avatar modeling that disentangles face and hair through a layered representation. Unlike previous UPMs that treat hair as an integral part of the head, our approach separates the modeling of the hairless head and hair into distinct branches. LUCAS is the first to introduce a mesh-based UPM, facilitating real-time rendering on devices. Our layered representation also improves the anchor geometry for precise and visually appealing Gaussian renderings. Experimental results indicate that LUCAS outperforms existing single-mesh and Gaussian-based avatar models in both quantitative and qualitative assessments, including evaluations on held-out subjects in zero-shot driving scenarios. LUCAS demonstrates superior dynamic performance in managing head pose changes, expression transfer, and hairstyle variations, thereby advancing the state-of-the-art in 3D head avatar reconstruction. |
| title | LUCAS: Layered Universal Codec Avatars |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.19739 |