# Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer

arXiv: https://arxiv.org/abs/2004.10955 · HTML: https://mlsanity.com/paper/2004.10955/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 30 of 31)

- [ARF: Artistic Radiance Fields](https://mlsanity.com/paper/2206.06360/) (2022)
- [UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene](https://mlsanity.com/paper/2208.07059/) (2022)
- [Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes](https://mlsanity.com/paper/2103.17185/) (2021)
- [Neural Preset for Color Style Transfer](https://mlsanity.com/paper/2303.13511/) (2023)
- [AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks](https://mlsanity.com/paper/2307.09724/) (2023)
- [PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals](https://mlsanity.com/paper/2110.11995/) (2021)
- [QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity](https://mlsanity.com/paper/2212.10431/) (2022)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization](https://mlsanity.com/paper/2303.13232/) (2023)
- [Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning](https://mlsanity.com/paper/2104.02230/) (2021)
- [Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation](https://mlsanity.com/paper/2208.14893/) (2022)
- [PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models](https://mlsanity.com/paper/2203.13452/) (2022)
- [NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer](https://mlsanity.com/paper/2303.09170/) (2023)
- [Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation](https://mlsanity.com/paper/2203.12835/) (2022)
- [Video Color Grading via Look-Up Table Generation](https://mlsanity.com/paper/2508.00548/) (2025)
- [Automatic Non-Linear Video Editing Transfer](https://mlsanity.com/paper/2105.06988/) (2021)
- [Direction-aware Video Demoireing with Temporal-guided Bilateral Learning](https://mlsanity.com/paper/2308.13388/) (2023)
- [AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer](https://mlsanity.com/paper/2212.01567/) (2022)
- [Goal Conditioned Reinforcement Learning for Photo Finishing Tuning](https://mlsanity.com/paper/2503.07300/) (2025)
- [Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer](https://mlsanity.com/paper/2304.04461/) (2023)
- [Multi-Curve Translator for High-Resolution Photorealistic Image Translation](https://mlsanity.com/paper/2203.07756/) (2022)
- [Guided Linear Upsampling](https://mlsanity.com/paper/2307.09582/) (2023)
- [StyleRetoucher: Generalized Portrait Image Retouching with GAN Priors](https://mlsanity.com/paper/2312.14389/) (2023)
- [Inharmonious Region Localization by Magnifying Domain Discrepancy](https://mlsanity.com/paper/2209.15368/) (2022)
- [Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer](https://mlsanity.com/paper/2607.08227/) (2026)
- [Line Search-Based Feature Transformation for Fast, Stable, and Tunable Content-Style Control in Photorealistic Style Transfer](https://mlsanity.com/paper/2210.05996/) (2022)
- [Advancements and Trends in Ultra-High-Resolution Image Processing: An Overview](https://mlsanity.com/paper/2312.00250/) (2023)
- [Uni-ISP: Toward Unifying the Learning of ISPs from Multiple Mobile Cameras](https://mlsanity.com/paper/2406.01003/) (2024)
- [MRStyle: A Unified Framework for Color Style Transfer with Multi-Modality Reference](https://mlsanity.com/paper/2409.05250/) (2024)
- [Hist2Style: Histogram-Guided Stylization with Bilateral Grids](https://mlsanity.com/paper/2606.01819/) (2026)

## Abstract (arXiv)

Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, produce impressive results but are either too slow to run at practical resolutions, or still contain objectionable artifacts. We propose a new end-to-end model for photorealistic style transfer that is both fast and inherently generates photorealistic results. The core of our approach is a feed-forward neural network that learns local edge-aware affine transforms that automatically obey the photorealism constraint. When trained on a diverse set of images and a variety of styles, our model can robustly apply style transfer to an arbitrary pair of input images. Compared to the state of the art, our method produces visually superior results and is three orders of magnitude faster, enabling real-time performance at 4K on a mobile phone. We validate our method with ablation and user studies.