  Recent 3D face reconstruction methods have made significant progress in shape
estimation, but high-fidelity facial albedo reconstruction remains challenging.
Existing methods depend on expensive light-stage captured data to learn facial
albedo maps. However, a lack of diversity in subjects limits their ability to
recover high-fidelity results. In this paper, we present a novel facial albedo
reconstruction model, HiFiAlbedo, which recovers the albedo map directly from a
single image without the need for captured albedo data. Our key insight is that
the albedo map is the illumination invariant texture map, which enables us to
use inexpensive texture data to derive an albedo estimation by eliminating
illumination. To achieve this, we first collect large-scale
ultra-high-resolution facial images and train a high-fidelity facial texture
codebook. By using the FFHQ dataset and limited UV textures, we then fine-tune
the encoder for texture reconstruction from the input image with adversarial
supervision in both image and UV space. Finally, we train a cross-attention
module and utilize group identity loss to learn the adaptation from facial
texture to the albedo domain. Extensive experimentation has demonstrated that
our method exhibits excellent generalizability and is capable of achieving
high-fidelity results for in-the-wild facial albedo recovery. Our code,
pre-trained weights, and training data will be made publicly available at
https://hifialbedo.github.io/.
