@inproceedings{chen-etal-2026-aesx,
title = "{A}es{X}: Enhance Your Images with Stunning Aesthetic Beauty",
author = "Chen, Yuyan and
Hou, Zhendong and
Xia, Lei and
Li, Jiahao and
Ji, Zhuolin and
Li, Zhixu",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.135/",
pages = "2001--2011",
ISBN = "979-8-89176-394-4",
abstract = "In the fields of advertising design, artistic creation, and cultural dissemination, there is an increasingly urgent demand for high-quality images that cater to fine-grained aesthetic preferences. Although existing large-scale models can generally meet basic requirements for clarity and alignment with textual elements, they still face significant bottlenecks in achieving precise control and aesthetic optimization. To address this limitation, we propose a set of comprehensive preference indicators across two major dimensions, text-image consistency and aesthetic quality, encompassing multiple criteria ranging from exposure and clarity to visual guidance and innovativeness. Building on these indicators, we have developed a generative framework named AesX to steer the model consistently toward a generation path that more closely aligns with human aesthetic sensibilities. Our experimental findings demonstrate that this approach yields significant improvements in both target recognition accuracy and overall visual aesthetic presentation."
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<abstract>In the fields of advertising design, artistic creation, and cultural dissemination, there is an increasingly urgent demand for high-quality images that cater to fine-grained aesthetic preferences. Although existing large-scale models can generally meet basic requirements for clarity and alignment with textual elements, they still face significant bottlenecks in achieving precise control and aesthetic optimization. To address this limitation, we propose a set of comprehensive preference indicators across two major dimensions, text-image consistency and aesthetic quality, encompassing multiple criteria ranging from exposure and clarity to visual guidance and innovativeness. Building on these indicators, we have developed a generative framework named AesX to steer the model consistently toward a generation path that more closely aligns with human aesthetic sensibilities. Our experimental findings demonstrate that this approach yields significant improvements in both target recognition accuracy and overall visual aesthetic presentation.</abstract>
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%0 Conference Proceedings
%T AesX: Enhance Your Images with Stunning Aesthetic Beauty
%A Chen, Yuyan
%A Hou, Zhendong
%A Xia, Lei
%A Li, Jiahao
%A Ji, Zhuolin
%A Li, Zhixu
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F chen-etal-2026-aesx
%X In the fields of advertising design, artistic creation, and cultural dissemination, there is an increasingly urgent demand for high-quality images that cater to fine-grained aesthetic preferences. Although existing large-scale models can generally meet basic requirements for clarity and alignment with textual elements, they still face significant bottlenecks in achieving precise control and aesthetic optimization. To address this limitation, we propose a set of comprehensive preference indicators across two major dimensions, text-image consistency and aesthetic quality, encompassing multiple criteria ranging from exposure and clarity to visual guidance and innovativeness. Building on these indicators, we have developed a generative framework named AesX to steer the model consistently toward a generation path that more closely aligns with human aesthetic sensibilities. Our experimental findings demonstrate that this approach yields significant improvements in both target recognition accuracy and overall visual aesthetic presentation.
%U https://aclanthology.org/2026.acl-industry.135/
%P 2001-2011
Markdown (Informal)
[AesX: Enhance Your Images with Stunning Aesthetic Beauty](https://aclanthology.org/2026.acl-industry.135/) (Chen et al., ACL 2026)
ACL
- Yuyan Chen, Zhendong Hou, Lei Xia, Jiahao Li, Zhuolin Ji, and Zhixu Li. 2026. AesX: Enhance Your Images with Stunning Aesthetic Beauty. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 2001–2011, San Diego, California, USA. Association for Computational Linguistics.