# AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer

arXiv: https://arxiv.org/abs/2212.01567 · HTML: https://mlsanity.com/paper/2212.01567/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 23 of 23)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [Photorealistic Style Transfer via Wavelet Transforms](https://mlsanity.com/paper/1903.09760/) (2019)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [A Closed-form Solution to Photorealistic Image Stylization](https://mlsanity.com/paper/1802.06474/) (2018)
- [Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration](https://mlsanity.com/paper/1805.03857/) (2018)
- [Learning Affinity via Spatial Propagation Networks](https://mlsanity.com/paper/1710.01020/) (2017)
- [HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms](https://mlsanity.com/paper/2011.11731/) (2020)
- [Learning Linear Transformations for Fast Arbitrary Style Transfer](https://mlsanity.com/paper/1808.04537/) (2018)
- [Ultrafast Photorealistic Style Transfer via Neural Architecture Search](https://mlsanity.com/paper/1912.02398/) (2019)
- [StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement](https://mlsanity.com/paper/2107.12898/) (2021)
- [Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer](https://mlsanity.com/paper/2004.10955/) (2020)
- [Very Lightweight Photo Retouching Network with Conditional Sequential Modulation](https://mlsanity.com/paper/2104.06279/) (2021)

## Built on by (most cited first, 7 of 7)

- [Video Color Grading via Look-Up Table Generation](https://mlsanity.com/paper/2508.00548/) (2025)
- [CanonCGT: Reference-Based Color Grading via Canonical Pivot Representation](https://mlsanity.com/paper/2606.01638/) (2026)
- [Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer](https://mlsanity.com/paper/2607.08227/) (2026)
- [MRStyle: A Unified Framework for Color Style Transfer with Multi-Modality Reference](https://mlsanity.com/paper/2409.05250/) (2024)
- [Image Translation with Kernel Prediction Networks for Semantic Segmentation](https://mlsanity.com/paper/2507.08554/) (2025)
- [ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching](https://mlsanity.com/paper/2607.07119/) (2026)
- [PrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences](https://mlsanity.com/paper/2609.34133/) (2026)

## Abstract (arXiv)

Photo-realistic style transfer aims at migrating the artistic style from an exemplar style image to a content image, producing a result image without spatial distortions or unrealistic artifacts. Impressive results have been achieved by recent deep models. However, deep neural network based methods are too expensive to run in real-time. Meanwhile, bilateral grid based methods are much faster but still contain artifacts like overexposure. In this work, we propose the Adaptive ColorMLP (AdaCM), an effective and efficient framework for universal photo-realistic style transfer. First, we find the complex non-linear color mapping between input and target domain can be efficiently modeled by a small multi-layer perceptron (ColorMLP) model. Then, in AdaCM, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair. Experimental results demonstrate that AdaCM can generate vivid and high-quality stylization results. Meanwhile, our AdaCM is ultrafast and can process a 4K resolution image in 6ms on one V100 GPU.