  We propose a zero-shot approach to image harmonization, aiming to overcome
the reliance on large amounts of synthetic composite images in existing
methods. These methods, while showing promising results, involve significant
training expenses and often struggle with generalization to unseen images. To
this end, we introduce a fully modularized framework inspired by human
behavior. Leveraging the reasoning capabilities of recent foundation models in
language and vision, our approach comprises three main stages. Initially, we
employ a pretrained vision-language model (VLM) to generate descriptions for
the composite image. Subsequently, these descriptions guide the foreground
harmonization direction of a text-to-image generative model (T2I). We refine
text embeddings for enhanced representation of imaging conditions and employ
self-attention and edge maps for structure preservation. Following each
harmonization iteration, an evaluator determines whether to conclude or modify
the harmonization direction. The resulting framework, mirroring human behavior,
achieves harmonious results without the need for extensive training. We present
compelling visual results across diverse scenes and objects, along with a user
study validating the effectiveness of our approach.
