  Learning-based image harmonization techniques are usually trained to undo
synthetic random global transformations applied to a masked foreground in a
single ground truth photo. This simulated data does not model many of the
important appearance mismatches (illumination, object boundaries, etc.) between
foreground and background in real composites, leading to models that do not
generalize well and cannot model complex local changes. We propose a new
semi-supervised training strategy that addresses this problem and lets us learn
complex local appearance harmonization from unpaired real composites, where
foreground and background come from different images. Our model is fully
parametric. It uses RGB curves to correct the global colors and tone and a
shading map to model local variations. Our method outperforms previous work on
established benchmarks and real composites, as shown in a user study, and
processes high-resolution images interactively.
