High-Quality Facial Albedo Generation for 3D Face Reconstruction from a Single Image using a Coarse-to-Fine Approach

TL;DR

This paper introduces a novel coarse-to-fine end-to-end method for generating high-quality facial UV albedo maps from a single image, significantly improving texture detail and realism in 3D face reconstruction.

Contribution

It presents a UV Albedo Parametric Model combined with a detail generator to produce high-resolution, high-frequency facial textures from minimal input data.

Findings

Outperforms existing methods in texture quality and realism

Successfully captures high-frequency details in UV albedo maps

Demonstrates high-fidelity 3D face reconstruction from a single image

Abstract

Facial texture generation is crucial for high-fidelity 3D face reconstruction from a single image. However, existing methods struggle to generate UV albedo maps with high-frequency details. To address this challenge, we propose a novel end-to-end coarse-to-fine approach for UV albedo map generation. Our method first utilizes a UV Albedo Parametric Model (UVAPM), driven by low-dimensional coefficients, to generate coarse albedo maps with skin tones and low-frequency texture details. To capture high-frequency details, we train a detail generator using a decoupled albedo map dataset, producing high-resolution albedo maps. Extensive experiments demonstrate that our method can generate high-fidelity textures from a single image, outperforming existing methods in terms of texture quality and realism. The code and pre-trained model are publicly available at https://github.com/MVIC-DAI/UVAPM, facilitating reproducibility and further research.