Core Concepts
The author proposes a zero-shot image harmonization method inspired by human behavior, leveraging pretrained generative models and textual descriptions to achieve satisfactory results without extensive training.
Abstract
The content introduces a novel approach to image harmonization that mimics human behavior, utilizing vision-language models and text-to-image generative models. By decomposing the task into imaging condition description generation, foreground region harmonization, and performance evaluation, the method achieves impressive results without heavy reliance on large datasets of composite images.
The approach is detailed through three main stages: generating descriptions for composite images using a vision-language model, guiding foreground harmonization with text-to-image models, and evaluating the harmonized results. The framework mirrors human reasoning processes and aims to bring inharmonious composite images closer to established priors without extensive training.
By optimizing text embeddings for accurate representation of imaging conditions and preserving content structure through self-attention maps and edge detection algorithms, the method ensures effective image harmonization. The effectiveness of the approach is demonstrated through qualitative examples, comparisons with state-of-the-art methods, and user preference evaluations.
Stats
Our method does not need to collect a large number of composite images for training.
We propose a zero-shot approach to image harmonization.
The dataset compiled for evaluation consisted of 300 composite images.
A total of 60,000 votes were collected in the user study.
The classifier used minimal computational cost for evaluation.
Quotes
"Our approach achieves satisfactory harmonized results without relying on extensive training on a large dataset of composite images."
"The framework mirrors human reasoning processes and aims to bring inharmonious composite images closer to established priors without extensive training."