Harmonized Portrait-Background Image Composition

Portrait-background image composition is a widely used operation in selfie editing, video meeting, and other portrait applications. To guarantee the realism of the composited images, the appearance of the foreground portraits needs to be adjusted to fit the new background images. Existing image harmonization approaches are proposed to handle general foreground objects, thus lack the special ability to adjust portrait foregrounds. In this paper, we present a novel end-to-end network architecture to learn both the content features and style features for portrait-background composition. The method adjusts the appearance of portraits to make them compatible with backgrounds, while the generation of the composited images satisfies the prior of a style-based generator. We also propose a pipeline to generate high-quality and high-variety synthesized image datasets for training and evaluation. The proposed method outperforms other state-of-the-art methods both on the synthesized dataset and the real composited images and shows robust performance in video applications.