# RefRetouch: Personalized Image Retouching without Test-time Fine-tuning

arXiv: https://arxiv.org/abs/2602.17044 · HTML: https://mlsanity.com/paper/2602.17044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [PrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences](https://mlsanity.com/paper/2609.34133/) (2026)

## Abstract (arXiv)

Personalized image retouching aims to adapt retouching styles of individual users from reference examples, but existing methods often require user-specific fine-tuning or fail to generalize effectively. To address these challenges, we introduce RefRetouch, a general framework for personalized image retouching that instantly adapts to user retouching styles without any test-time fine-tuning. It employs an textitasymmetric auto-encoder to encode the retouching style from paired examples into a content disentangled latent representation that enables faithful transfer of the retouching style to new images. To adaptively apply the encoded retouching style to new images, we further propose textitretrieval-augmented retouching (RAR), which retrieves and aggregates style latents from reference pairs most similar in content to the query image. With these components, RefRetouch enables superior and generic content-aware retouching personalization across diverse scenarios, including single-reference, multi-reference, and mixed-style settings, while also generalizing out of the box to photorealistic style transfer.