# ReCoNet: Real-time Coherent Video Style Transfer Network

arXiv: https://arxiv.org/abs/1807.01197 · HTML: https://mlsanity.com/paper/1807.01197/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 26 of 26)

- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [Stylizing 3D Scene via Implicit Representation and HyperNetwork](https://mlsanity.com/paper/2105.13016/) (2021)
- [Learning to Stylize Novel Views](https://mlsanity.com/paper/2105.13509/) (2021)
- [StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions](https://mlsanity.com/paper/2112.01530/) (2021)
- [CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer](https://mlsanity.com/paper/2207.04808/) (2022)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [SNeRF: Stylized Neural Implicit Representations for 3D Scenes](https://mlsanity.com/paper/2207.02363/) (2022)
- [StyleFool: Fooling Video Classification Systems via Style Transfer](https://mlsanity.com/paper/2203.16000/) (2022)
- [Gaussian Splatting in Style](https://mlsanity.com/paper/2403.08498/) (2024)
- [I2V-GAN: Unpaired Infrared-to-Visible Video Translation](https://mlsanity.com/paper/2108.00913/) (2021)
- [Depth-aware Neural Style Transfer using Instance Normalization](https://mlsanity.com/paper/2203.09242/) (2022)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow](https://mlsanity.com/paper/2201.05723/) (2022)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [MuVieCAST: Multi-View Consistent Artistic Style Transfer](https://mlsanity.com/paper/2312.05046/) (2023)
- [Neural Style Transfer for Computer Games](https://mlsanity.com/paper/2311.14617/) (2023)
- [G3DST: Generalizing 3D Style Transfer with Neural Radiance Fields across Scenes and Styles](https://mlsanity.com/paper/2408.13508/) (2024)
- [Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer](https://mlsanity.com/paper/2503.04707/) (2025)
- [WAIT: Feature Warping for Animation to Illustration video Translation using GANs](https://mlsanity.com/paper/2310.04901/) (2023)
- [PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping](https://mlsanity.com/paper/2403.08252/) (2024)
- [Adapting MIMO video restoration networks to low latency constraints](https://mlsanity.com/paper/2408.12439/) (2024)
- [PQDAST: Depth-Aware Arbitrary Style Transfer for Games via Perceptual Quality-Guided Distillation](https://mlsanity.com/paper/2502.16996/) (2025)
- [Instant Video Models: Universal Adapters for Stabilizing Image-Based Networks](https://mlsanity.com/paper/2512.03014/) (2025)
- [M2StyleGS: Multi-Modality 3D Style Transfer with Gaussian Splatting](https://mlsanity.com/paper/2604.03773/) (2026)
- [EchoStyle: Unlocking High-Fidelity Video Stylization with Reverse Data Synthesis](https://mlsanity.com/paper/2606.25465/) (2026)
- [SPECSIA: Stylization Dataset for Novel-View Enhancement in Drawing-based 3D Animation](https://mlsanity.com/paper/2607.00525/) (2026)

## Abstract (arXiv)

Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles. A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects. We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively.