InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering

• URL: http://arxiv.org/abs/2308.04868v3
• Date: Fri, 5 Apr 2024 09:03:48 GMT
• Title: InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering
• Authors: Antonio Canela, Pol Caselles, Ibrar Malik, Eduard Ramon, Jaime García, Jordi Sánchez-Riera, Gil Triginer, Francesc Moreno-Noguer,
• Abstract summary: We introduce InstantAvatar, a method that recovers full-head avatars from few images (down to just one) in a few seconds on commodity hardware. We present a novel statistical model that learns a prior distribution over 3D head signed distance functions using a voxel-grid based architecture.
• Score: 13.85652935706768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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