  Despite recent breakthroughs in deep learning methods for image lighting
enhancement, they are inferior when applied to portraits because 3D facial
information is ignored in their models. To address this, we present a novel
deep learning framework for portrait lighting enhancement based on 3D facial
guidance. Our framework consists of two stages. In the first stage, corrected
lighting parameters are predicted by a network from the input bad lighting
image, with the assistance of a 3D morphable model and a differentiable
renderer. Given the predicted lighting parameter, the differentiable renderer
renders a face image with corrected shading and texture, which serves as the 3D
guidance for learning image lighting enhancement in the second stage. To better
exploit the long-range correlations between the input and the guidance, in the
second stage, we design an image-to-image translation network with a novel
transformer architecture, which automatically produces a lighting-enhanced
result. Experimental results on the FFHQ dataset and in-the-wild images show
that the proposed method outperforms state-of-the-art methods in terms of both
quantitative metrics and visual quality. We will publish our dataset along with
more results on https://cassiepython.github.io/egsr/index.html.
