Capture, Canonicalize, Splat: Zero-Shot 3D Gaussian Avatars from Unstructured Phone Images
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917044448919552 |
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| author | Garbin, Emanuel Adam, Guy Krams, Oded Barzelay, Zohar Guendelman, Eran Schwarz, Michael Presutto, Matteo Vatelmacher, Moran Shenkman, Yigal Peker, Eli Druker, Itai Patish, Uri Blum, Yoav Bluvstein, Max Li, Junxuan Khirodkar, Rawal Saito, Shunsuke |
| author_facet | Garbin, Emanuel Adam, Guy Krams, Oded Barzelay, Zohar Guendelman, Eran Schwarz, Michael Presutto, Matteo Vatelmacher, Moran Shenkman, Yigal Peker, Eli Druker, Itai Patish, Uri Blum, Yoav Bluvstein, Max Li, Junxuan Khirodkar, Rawal Saito, Shunsuke |
| contents | We present a novel, zero-shot pipeline for creating hyperrealistic, identity-preserving 3D avatars from a few unstructured phone images. Existing methods face several challenges: single-view approaches suffer from geometric inconsistencies and hallucinations, degrading identity preservation, while models trained on synthetic data fail to capture high-frequency details like skin wrinkles and fine hair, limiting realism. Our method introduces two key contributions: (1) a generative canonicalization module that processes multiple unstructured views into a standardized, consistent representation, and (2) a transformer-based model trained on a new, large-scale dataset of high-fidelity Gaussian splatting avatars derived from dome captures of real people. This "Capture, Canonicalize, Splat" pipeline produces static quarter-body avatars with compelling realism and robust identity preservation from unstructured photos. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Capture, Canonicalize, Splat: Zero-Shot 3D Gaussian Avatars from Unstructured Phone Images Garbin, Emanuel Adam, Guy Krams, Oded Barzelay, Zohar Guendelman, Eran Schwarz, Michael Presutto, Matteo Vatelmacher, Moran Shenkman, Yigal Peker, Eli Druker, Itai Patish, Uri Blum, Yoav Bluvstein, Max Li, Junxuan Khirodkar, Rawal Saito, Shunsuke Computer Vision and Pattern Recognition Graphics We present a novel, zero-shot pipeline for creating hyperrealistic, identity-preserving 3D avatars from a few unstructured phone images. Existing methods face several challenges: single-view approaches suffer from geometric inconsistencies and hallucinations, degrading identity preservation, while models trained on synthetic data fail to capture high-frequency details like skin wrinkles and fine hair, limiting realism. Our method introduces two key contributions: (1) a generative canonicalization module that processes multiple unstructured views into a standardized, consistent representation, and (2) a transformer-based model trained on a new, large-scale dataset of high-fidelity Gaussian splatting avatars derived from dome captures of real people. This "Capture, Canonicalize, Splat" pipeline produces static quarter-body avatars with compelling realism and robust identity preservation from unstructured photos. |
| title | Capture, Canonicalize, Splat: Zero-Shot 3D Gaussian Avatars from Unstructured Phone Images |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2510.14081 |