Language Guidance Generation Using Aesthetic Attribute Comparison for Human Photography and AIGC

Xin Jin, Qianqian Qiao, Qiang Deng, Chaoen Xiao, Heng Huang, Hao Lou

Abstract: With the proliferation of mobile photography technology, leading mobile phone manufacturers are racing to enhance the shooting capabilities of their equipment and the photo beautification algorithm of their software. However, the development of intelligent equipment and algorithms cannot supplant human subjective photography techniques. Simultaneously, with the rapid advancement of AIGC technology, AI simulation shooting has become an integral part of people's daily lives. If it were possible to assist human photography and AIGC with language guidance, this would be a significant step forward in subjectively improving the aesthetic quality of photographic images. In this paper, we propose Aesthetic Language Guidance of Image (ALG) and present a series of language guidance rules (ALG Rules). ALG is divided into ALG-T and ALG-I based on whether the guiding rules are derived from photography templates or reference images, respectively. ALG-T and ALG-I both provide guidance for photography based on three attributes of color, light, and composition of images. ALG-T and ALG-I provide aesthetic language guidance for two types of input images, landscape and portrait images. We employ two methods to conduct confirmatory experiments, human photography, and AIGC imitation shooting. In the experiments, by comparing the aesthetic scores of original and modified images, the results show that our proposed guidance scheme significantly improves the aesthetic quality of photos in terms of color, composition, and lighting attributes.