InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering

TL;DR

InstantAvatar is a novel method that rapidly reconstructs full-head 3D avatars from minimal images using a voxel-grid neural field and a learned prior, achieving high accuracy in seconds.

Contribution

It introduces a fast 3D head reconstruction system combining voxel-grid neural fields with a statistical prior, significantly reducing computation time.

Findings

Achieves 3D head reconstructions in seconds.

Maintains comparable accuracy to state-of-the-art methods.

Uses a learned prior to stabilize and accelerate optimization.

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.