FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video

TL;DR

FATE is a novel method that reconstructs fully editable, 360-degree 3D head avatars from monocular videos, addressing incomplete reconstructions and representation inefficiencies with innovative sampling, neural baking, and completion techniques.

Contribution

FATE introduces a comprehensive framework combining sampling densification, neural baking, and universal completion for high-quality, editable 3D head avatars from monocular videos, achieving state-of-the-art results.

Findings

Outperforms previous methods in qualitative and quantitative evaluations.

Achieves state-of-the-art performance in 3D head avatar reconstruction.

First method for fully animatable, 360-degree monocular head avatars.

Abstract

Reconstructing high-fidelity, animatable 3D head avatars from effortlessly captured monocular videos is a pivotal yet formidable challenge. Although significant progress has been made in rendering performance and manipulation capabilities, notable challenges remain, including incomplete reconstruction and inefficient Gaussian representation. To address these challenges, we introduce FATE, a novel method for reconstructing an editable full-head avatar from a single monocular video. FATE integrates a sampling-based densification strategy to ensure optimal positional distribution of points, improving rendering efficiency. A neural baking technique is introduced to convert discrete Gaussian representations into continuous attribute maps, facilitating intuitive appearance editing. Furthermore, we propose a universal completion framework to recover non-frontal appearance, culminating in a 360$^\circ$-renderable 3D head avatar. FATE outperforms previous approaches in both qualitative and quantitative evaluations, achieving state-of-the-art performance. To the best of our knowledge, FATE is the first animatable and 360$^\circ$ full-head monocular reconstruction method for a 3D head avatar.