@inproceedings{
li2024grefine,
title={G-Refine: A General Refiner for Text-to-Image Generation},
author={Chunyi Li and Haoning Wu and Hongkun Hao and Zicheng Zhang and Tengchuan Kou and Chaofeng Chen and Xiaohong Liu and LEI BAI and Weisi Lin and Guangtao Zhai},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=sOxLJfQYI0}
}
With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier to their widespread adoption. In terms of both perception and alignment, existing models cannot always guarantee high-quality results. To mitigate this limitation, we introduce G-Refine, a general image quality refiner designed to enhance low-quality images without compromising the integrity of high-quality ones. The model is composed of three interconnected modules: a perception quality indicator, an alignment quality indicator, and a general quality enhancement module. Based on the mechanisms of the Human Visual System (HVS) and syntax trees, the first two indicators can respectively identify the perception and alignment deficiencies, and the last module can apply targeted quality enhancement accordingly. Extensive experimentation reveals that when compared to alternative optimization methods, AIGIs after G-Refine outperform in 10+ quality metrics across 4 datasets. This improvement significantly contributes to the practical application of contemporary T2I models, paving the way for their broader adoption.
G-Refine: A General Refiner for Text-to-Image Generation
Chunyi Li Haoning Wu Hongkun Hao Zicheng Zhang Tengchuan Kou Chaofeng Chen Xiaohong Liu LEI BAI Weisi Lin Guangtao Zhai