论文标题
图像光源操纵的深度重新确认网络
Deep Relighting Networks for Image Light Source Manipulation
论文作者
论文摘要
操纵给定图像的光源是一项有趣的任务，在包括摄影和摄影在内的各种应用中都有用。现有方法通常需要其他信息，例如场景的几何结构，这对于大多数图像可能无法使用。在本文中，我们制定了单一图像重新完成任务，并提出了一个新颖的深度重新确认网络（DRN），其中三个部分：1）场景重新进行回归，旨在通过深层自动编码器网络（2）先进的估算来揭示主要场景结构，以预测新光线从新的光线中预测通过对对抗性学习的新光线，以及估算范围的范围，以将其重新构造与重新构建的范围结合，以构成重新构造的范围。实验结果表明，所提出的方法在定性和定量上都优于其他可能的方法。具体而言，拟议的DRN在2020年ECCV会议上实现了“ AIM2020的最佳挑战”中最好的PSNR。
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.
