Learning Highlight Separation of Real High Resolution Portrait Image

Abstract

∗This work presents an approach for highlight separation of real high resolution portrait image. In order to obtain reliable ground truth of real images, a controllable portrait image collection system with 156 groups of light sources has been built. It has 4 cameras to collect the portrait images of 36 subjects from different angles, and then we use 4 data processing strategies on these images to obtain 4 training datasets. Based on these datasets, 4 U-Net networks are trained by using a single image as input. To test and evaluate, we input the 2560*2560 resolution images into 4 models, and finally determine the best data processing strategy and trained network. Our method creates precise and believable highlight separation results for 2560*2560 high resolution images, including when the subject is not looking straight at the camera.