Existing research has made impressive strides in reconstructing human facial shapes and textures from images with well-illuminated faces and minimal external occlusions. Nevertheless, it remains challenging to recover accurate facial textures from scenarios with complicated illumination affected by external occlusions, e.g. a face that is partially obscured by items such as a hat. Existing works based on the assumption of single and uniform illumination cannot correctly process these data. In this work, we introduce a novel approach to model 3D facial textures under such unnatural illumination. Instead of assuming single illumination, our framework learns to imitate the unnatural illumination as a composition of multiple separate light conditions combined with learned neural representations, named Light Decoupling. According to experiments on both single images and video sequences, we demonstrate the effectiveness of our approach in modeling facial textures under challenging illumination affected by occlusions. Please check https://tianxinhuang.github.io/projects/Deface for our videos and codes.

翻译：现有研究在从光照良好且外部遮挡最小的人脸图像中重建人脸形状和纹理方面取得了显著进展。然而，在受到外部遮挡影响的复杂光照场景中（例如部分被帽子等物品遮挡的人脸），恢复精确的人脸纹理仍然具有挑战性。基于单一均匀光照假设的现有方法无法正确处理此类数据。在本工作中，我们提出了一种新颖的方法来建模此类非自然光照下的三维人脸纹理。我们的框架摒弃了单一光照假设，通过学习将非自然光照模拟为多种独立光照条件与习得的神经表征的组合，这一方法被命名为光源解耦。通过在单张图像和视频序列上的实验，我们证明了该方法在受遮挡影响的挑战性光照条件下对人脸纹理建模的有效性。请访问 https://tianxinhuang.github.io/projects/Deface 查看我们的视频和代码。