InstantRetouch: Personalized Image Retouching without Test-time Fine-tuning

摘要

Personalized image retouching aims to adapt retouching stylesof individual users from reference examples, but existing methods oftenrequire user-specific fine-tuning or fail to generalize effectively. To addressthese challenges, we introduce RefRetouch, a general framework forpersonalized image retouching that instantly adapts to user retouchingstyles without any test-time fine-tuning. It employs an asymmetric auto-encoder to encode the retouching style from paired examples into a con-tent disentangled latent representation that enables faithful transfer ofthe retouching style to new images. To adaptively apply the encoded re-touching style to new images, we further propose retrieval-augmented re-touching (RAR), which retrieves and aggregates style latents from refer-ence pairs most similar in content to the query image. With these compo-nents, RefRetouch enables superior and generic content-aware retouch-ing personalization across diverse scenarios, including single-reference,multi-reference, and mixed-style settings, while also generalizing out ofthe box to photorealistic style transfer.