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Painterly image harmonization aims to harmonize a photographic foregroundobject on the painterly background. Different from previous auto-encoder basedharmonization networks, we develop a progressive multi-stage harmonizationnetwork, which harmonizes the composite foreground from low-level styles (e.g.,color, simple texture) to high-level styles (e.g., complex texture). Ournetwork has better interpretability and harmonization performance. Moreover, wedesign an early-exit strategy to automatically decide the proper stage to exit,which can skip the unnecessary and even harmful late stages. Extensiveexperiments on the benchmark dataset demonstrate the effectiveness of ourprogressive harmonization network.

Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles