  Manipulating the light source of given images is an interesting task and
useful in various applications, including photography and cinematography.
Existing methods usually require additional information like the geometric
structure of the scene, which may not be available for most images. In this
paper, we formulate the single image relighting task and propose a novel Deep
Relighting Network (DRN) with three parts: 1) scene reconversion, which aims to
reveal the primary scene structure through a deep auto-encoder network, 2)
shadow prior estimation, to predict light effect from the new light direction
through adversarial learning, and 3) re-renderer, to combine the primary
structure with the reconstructed shadow view to form the required estimation
under the target light source. Experimental results show that the proposed
method outperforms other possible methods, both qualitatively and
quantitatively. Specifically, the proposed DRN has achieved the best PSNR in
the "AIM2020 - Any to one relighting challenge" of the 2020 ECCV conference.
