Painterly Image Harmonization by Learning from Painterly Objects
Li Niu Junyan Cao Yan Hong Liqing Zhang
Given a composite image with photographic object and painterly background,
painterly image harmonization targets at stylizing the composite object to be
compatible with the background. Despite the competitive performance of existing
painterly harmonization works, they did not fully leverage the painterly
objects in artistic paintings. In this work, we explore learning from painterly
objects for painterly image harmonization. In particular, we learn a mapping
from background style and object information to object style based on painterly
objects in artistic paintings. With the learnt mapping, we can hallucinate the
target style of composite object, which is used to harmonize encoder feature
maps to produce the harmonized image. Extensive experiments on the benchmark
dataset demonstrate the effectiveness of our proposed method.