Recent image harmonization methods have demonstrated promising results.
However, due to their heavy reliance on a large number of composite images,
these works are expensive in the training phase and often fail to generalize to
unseen images. In this paper, we draw lessons from human behavior and come up
with a zero-shot image harmonization method. Specifically, in the harmonization
process, a human mainly utilizes his long-term prior on harmonious images and
makes a composite image close to that prior. To imitate that, we resort to
pretrained generative models for the prior of natural images. For the guidance
of the harmonization direction, we propose an Attention-Constraint Text which
is optimized to well illustrate the image environments. Some further designs
are introduced for preserving the foreground content structure. The resulting
framework, highly consistent with human behavior, can achieve harmonious
results without burdensome training. Extensive experiments have demonstrated
the effectiveness of our approach, and we have also explored some interesting
applications.

该文提出了一种零样本图片和谐方法，该方法利用了人类长期对和谐图片的先验知识，并通过预训练的生成模型来实现先验，同时引入注意力约束文本来指导和谐方向，并设计一定的方法来保持前景内容结构。实验表明了该方法的有效性。