Real-time shadow-aware portrait relighting in virtual backgrounds for realistic telepresence

Abstract

While using virtual backgrounds has recently become a very popular feature in videoconferencing, there often exists a jarring mismatch between the lighting of the user and the illumination condition of the virtual background. Existing portrait relighting methods can alleviate the problem, but do not have the capacity to deal with difficult shadow effects. In this paper, we present a new shadow-aware portrait relighting system that can relight an input portrait to be consistent with a given desired background image with shadow effects. Our system consists of four major components: portrait neutralization, illumination estimation, shadow generation and hierarchical neural rendering, which are all based on deep neural networks, and the whole system is end-to-end trainable. In addition, we created a large-scale photorealistic synthetic dataset with shadow, illumination and depth annotations for training, which allows our model to generalize well to real images. The extensive experiments demonstrate that our shadow-aware relight system outperforms the state-of-the-art portrait relighting solutions in terms of producing more lighting-consistent relighted images with shadow effects.