InstantRetouch: Personalized Image Retouching without Test-time Fine-tuning Using an Asymmetric Auto-Encoder

2025-11-12

arXiv.org

AI Summary

本文提出RefRetouch框架，通过不对称自编码器与检索增强修图技术解决个性化图像修饰过程中无需微调即可适应用户风格的问题。

Abstract

Personalized image retouching aims to adapt retouching style 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 $\textbf{InstantRetouch}$, a general framework for personalized image retouching that instantly adapts to user retouching styles without any test-time fine-tuning. It employs an $\textit{asymmetric auto-encoder}$ 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 $\textit{retrieval-augmented retouching}$ (RAR), which retrieves and aggregates style latents from reference pairs most similar in content to the query image. With these components, $\textbf{InstantRetouch}$ enables superior and generic content-aware retouching personalization across diverse scenarios, including single-reference, multi-reference, and mixed-style setups, while also generalizing out of the box to photorealistic style transfer.

Problem

• personalized image retouching
• user-specific fine-tuning
• generalization

Innovation

• asymmetric auto-encoder
• retrieval-augmented retouching
• content disentangled latent representation