Publication

InstantAvatar efficient 3D head reconstruction via surface rendering

Conference Article

Conference

International Conference on 3D Vision (3DV)

Edition

2024

Pages

995-1005

Doc link

https://doi.org/10.1109/3DV62453.2024.00071

File

Download the digital copy of the doc pdf document

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.

Categories

computer vision.

Author keywords

3D, reconstruction, single-view, multi-view, deep learning, neural, rendering

Scientific reference

A. Canela, P. Caselles, I. Malik, E. Ramon, J. García, J. Sanchez, G. Triginer and F. Moreno-Noguer. InstantAvatar efficient 3D head reconstruction via surface rendering, 2024 International Conference on 3D Vision, 2024, , pp. 995-1005.