InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2023
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| author | Canela, Antonio Caselles, Pol Malik, Ibrar Ramon, Eduard García, Jaime Sánchez-Riera, Jordi Triginer, Gil Moreno-Noguer, Francesc |
| author_facet | Canela, Antonio Caselles, Pol Malik, Ibrar Ramon, Eduard García, Jaime Sánchez-Riera, Jordi Triginer, Gil Moreno-Noguer, Francesc |
| contents | Recent advances in full-head reconstruction have been obtained by optimizing a neural field through differentiable surface or volume rendering to represent a single scene. While these techniques achieve an unprecedented accuracy, they take several minutes, or even hours, due to the expensive optimization process required. In this work, we introduce InstantAvatar, a method that recovers full-head avatars from few images (down to just one) in a few seconds on commodity hardware. In order to speed up the reconstruction process, we propose a system that combines, for the first time, a voxel-grid neural field representation with a surface renderer. Notably, a naive combination of these two techniques leads to unstable optimizations that do not converge to valid solutions. In order to overcome this limitation, we present a novel statistical model that learns a prior distribution over 3D head signed distance functions using a voxel-grid based architecture. The use of this prior model, in combination with other design choices, results into a system that achieves 3D head reconstructions with comparable accuracy as the state-of-the-art with a 100x speed-up. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_04868 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering Canela, Antonio Caselles, Pol Malik, Ibrar Ramon, Eduard García, Jaime Sánchez-Riera, Jordi Triginer, Gil Moreno-Noguer, Francesc Computer Vision and Pattern Recognition Recent advances in full-head reconstruction have been obtained by optimizing a neural field through differentiable surface or volume rendering to represent a single scene. While these techniques achieve an unprecedented accuracy, they take several minutes, or even hours, due to the expensive optimization process required. In this work, we introduce InstantAvatar, a method that recovers full-head avatars from few images (down to just one) in a few seconds on commodity hardware. In order to speed up the reconstruction process, we propose a system that combines, for the first time, a voxel-grid neural field representation with a surface renderer. Notably, a naive combination of these two techniques leads to unstable optimizations that do not converge to valid solutions. In order to overcome this limitation, we present a novel statistical model that learns a prior distribution over 3D head signed distance functions using a voxel-grid based architecture. The use of this prior model, in combination with other design choices, results into a system that achieves 3D head reconstructions with comparable accuracy as the state-of-the-art with a 100x speed-up. |
| title | InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.04868 |