从单图生成高保真人脸纹理，提升细节真实感
High-Quality Facial Albedo Generation for 3D Face Reconstruction from a Single Image using a Coarse-to-Fine Approach
- 分步生成：先粗后细，先定肤色再补细节
- 生成的纹理在视觉质量上超越现有方法
- 适合需要高质量人脸建模的研究与应用
从单张图像进行高保真3D人脸重建的关键在于面部纹理生成。然而，现有方法难以生成具有高频细节的UV反照率图。为此，我们提出一种端到端的粗到精方法来生成UV反照率图。首先利用低维系数驱动的UV反照率参数化模型（UVAPM）生成包含肤色和低频纹理的粗略反照率图；为捕捉高频细节，使用解耦反照率图数据集训练一个细节生成器，输出高分辨率反照率图。大量实验证明，该方法可从单图生成高保真纹理，在纹理质量和真实感方面优于现有方法。代码与预训练模型已公开于https://github.com/MVIC-DAI/UVAPM，便于复现与后续研究。
原文摘要 · Abstract (English)
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.
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