Composite Photograph Harmonization with Complete Background Cues

Abstract

Compositing portrait photographs or videos to novel backgrounds is an important application in computational photography. Seamless blending along boundaries and globally harmonic colors are two desired properties of the photo-realistic composition of foregrounds and new backgrounds. Existing works are dedicated to either foreground alpha matte generation or after-blending harmonization, leading to sub-optimal background replacement when putting foregrounds and backgrounds together. In this work, we unify the two objectives in a single framework to obtain realistic portrait image composites. Specifically, we investigate the usage of a target background and find that a complete background plays a vital role in both seamlessly blending and harmonization. We develop a network to learn the composition process given an imperfect alpha matte with appearance features extracted from the complete background to adjust color distribution. Our dedicated usage of a complete background enables realistic portrait image composition and also temporally stable results on videos. Extensive quantitative and qualitative experiments on both synthetic and real-world data demonstrate that our method achieves state-of-the-art performance.