Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles
Painterly image harmonization aims to harmonize a photographic foreground object on the painterly background. Different from previous auto-encoder based harmonization networks, we develop a progressive multi-stage harmonization network, which harmonizes the composite foreground from low-level styles (e.g., color, simple texture) to high-level styles (e.g., complex texture). Our network has better interpretability and harmonization performance. Moreover, we design an early-exit strategy to automatically decide the proper stage to exit, which can skip the unnecessary and even harmful late stages. Extensive experiments on the benchmark dataset demonstrate the effectiveness of our progressive harmonization network.
Code (1)
Tasks
Image HarmonizationSimilar Papers 제목 키워드 기반
Painterly Image Harmonization by Learning from Painterly Objects
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…
Image HarmonizationObjectPainterly Image Harmonization in Dual Domains
Image harmonization aims to produce visually harmonious composite images by adjusting the foreground appearance to be compatible with the background. When the composite image has photographic foreground and painterly bac…
Image HarmonizationPainterly Image Harmonization using Diffusion Model
Painterly image harmonization aims to insert photographic objects into paintings and obtain artistically coherent composite images. Previous methods for this task mainly rely on inference optimization or generative adver…
Generative Adversarial NetworkImage HarmonizationInference OptimizationmodelTraining-and-Prompt-Free General Painterly Harmonization via Zero-Shot Disentenglement on Style and Content References
Painterly image harmonization aims at seamlessly blending disparate visual elements within a single image. However, previous approaches often struggle due to limitations in training data or reliance on additional prompts…
Image HarmonizationFreePIH: Training-Free Painterly Image Harmonization with Diffusion Model
This paper provides an efficient training-free painterly image harmonization (PIH) method, dubbed FreePIH, that leverages only a pre-trained diffusion model to achieve state-of-the-art harmonization results. Unlike exist…
DenoisingImage HarmonizationStyle Transfer