Personalized Image Filter: Mastering Your Photographic Style

Chengxuan Zhu, Shuchen Weng, Jiacong Fang, Peixuan Zhang, Si Li +2 more
10/19/2025
cs.CV

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Abstract

Photographic style, as a composition of certain photographic concepts, is the charm behind renowned photographers. But learning and transferring photographic style need a profound understanding of how the photo is edited from the unknown original appearance. Previous works either fail to learn meaningful photographic concepts from reference images, or cannot preserve the content of the content image. To tackle these issues, we proposed a Personalized Image Filter (PIF). Based on a pretrained text-to-image diffusion model, the generative prior enables PIF to learn the average appearance of photographic concepts, as well as how to adjust them according to text prompts. PIF then learns the photographic style of reference images with the textual inversion technique, by optimizing the prompts for the photographic concepts. PIF shows outstanding performance in extracting and transferring various kinds of photographic style. Project page: https://pif.pages.dev/

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Cite this paper

@article{zhu2025personalized,
  title  = {Personalized Image Filter: Mastering Your Photographic Style},
  author = {Chengxuan Zhu and Shuchen Weng and Jiacong Fang and Peixuan Zhang and Si Li and Chao Xu and Boxin Shi},
  year   = {2025},
  eprint = {2510.16791},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.16791v1}
}

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