# PrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences

arXiv: https://arxiv.org/abs/2609.34133 · HTML: https://mlsanity.com/paper/2609.34133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 22 of 22)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables](https://mlsanity.com/paper/2108.08697/) (2021)
- [PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency](https://mlsanity.com/paper/2105.09180/) (2021)
- [SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement](https://mlsanity.com/paper/2207.08351/) (2022)
- [Neural Preset for Color Style Transfer](https://mlsanity.com/paper/2303.13511/) (2023)
- [4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement](https://mlsanity.com/paper/2209.01749/) (2022)
- [StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement](https://mlsanity.com/paper/2107.12898/) (2021)
- [NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement](https://mlsanity.com/paper/2306.11920/) (2023)
- [Deep Preset: Blending and Retouching Photos with Color Style Transfer](https://mlsanity.com/paper/2007.10701/) (2020)
- [DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts](https://mlsanity.com/paper/2407.03757/) (2024)
- [SA-LUT: Spatial Adaptive 4D Look-Up Table for Photorealistic Style Transfer](https://mlsanity.com/paper/2506.13465/) (2025)
- [AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer](https://mlsanity.com/paper/2212.01567/) (2022)
- [Personalized Image Enhancement Featuring Masked Style Modeling](https://mlsanity.com/paper/2306.09334/) (2023)
- [PerTouch: VLM-Driven Agent for Personalized and Semantic Image Retouching](https://mlsanity.com/paper/2511.12998/) (2025)
- [Efficient Neural Network Encoding for 3D Color Lookup Tables](https://mlsanity.com/paper/2412.15438/) (2024)
- [FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching](https://mlsanity.com/paper/2509.23608/) (2025)
- [AceTone: Bridging Words and Colors for Conditional Image Grading](https://mlsanity.com/paper/2604.00530/) (2026)
- [RefRetouch: Personalized Image Retouching without Test-time Fine-tuning](https://mlsanity.com/paper/2602.17044/) (2025)
- [Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer](https://mlsanity.com/paper/2607.08227/) (2026)
- [Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement](https://mlsanity.com/paper/2607.27659/) (2026)

## Abstract (arXiv)

Photographic color editing is inherently personal: the same image can appear too warm, too muted, or already satisfactory to different users. Most lookup table (LUT) and reference-guided methods target a specified appearance rather than model persistent preferences from repeated user choices. To address this gap, we introduce PrefLUT, a reusable and refinable user-preference modeling framework for deployable 3D LUTs, encoding ordered preferred/non-preferred image pairs into a lightweight Reusable User Profile that is reused across queries and refined using additional user preference pairs, without per-user optimization. A Query-Conditioned LUT Predictor combines this profile with each image to predict a LUT latent vector and edit strength. An Identity-Residual LUT Decoder and Edit-Strength Controller then produce an exportable 3D LUT. Experiments on three datasets demonstrate effective personalized editing and general-purpose enhancement. Each quantized profile requires only 260 bytes, and editing takes 1.365 ms/image on an RTX 5090 GPU. We also introduce the Preference-Conditioning Verification Protocol (PCVP), an evaluation protocol to verify whether personalized image edits depend on user preferences and the query image through controlled changes to user profiles, preference orders, pair correspondences, and query images.