# Artistic style transfer for videos and spherical images

arXiv: https://arxiv.org/abs/1708.04538 · HTML: https://mlsanity.com/paper/1708.04538/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2017.

## Built on by (most cited first, 30 of 45)

- [Point-Based Neural Rendering with Per-View Optimization](https://mlsanity.com/paper/2109.02369/) (2021)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [ARF: Artistic Radiance Fields](https://mlsanity.com/paper/2206.06360/) (2022)
- [StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields](https://mlsanity.com/paper/2303.10598/) (2023)
- [StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions](https://mlsanity.com/paper/2112.01530/) (2021)
- [ArtFID: Quantitative Evaluation of Neural Style Transfer](https://mlsanity.com/paper/2207.12280/) (2022)
- [CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer](https://mlsanity.com/paper/2207.04808/) (2022)
- [3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image](https://mlsanity.com/paper/2112.00169/) (2021)
- [Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization](https://mlsanity.com/paper/2212.02766/) (2022)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [SketchVideo: Sketch-based Video Generation and Editing](https://mlsanity.com/paper/2503.23284/) (2025)
- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network](https://mlsanity.com/paper/2202.08010/) (2022)
- [Reference-based Controllable Scene Stylization with Gaussian Splatting](https://mlsanity.com/paper/2407.07220/) (2024)
- [Depth-aware Neural Style Transfer using Instance Normalization](https://mlsanity.com/paper/2203.09242/) (2022)
- [Video Color Grading via Look-Up Table Generation](https://mlsanity.com/paper/2508.00548/) (2025)
- [OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression](https://mlsanity.com/paper/2107.09179/) (2021)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization](https://mlsanity.com/paper/2308.12452/) (2023)
- [ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields](https://mlsanity.com/paper/2402.01950/) (2024)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [Frame Difference-Based Temporal Loss for Video Stylization](https://mlsanity.com/paper/2102.05822/) (2021)
- [SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data](https://mlsanity.com/paper/2207.08979/) (2022)
- [Query-Efficient Video Adversarial Attack with Stylized Logo on Service Computing](https://mlsanity.com/paper/2408.12099/) (2024)
- [4DStyleGaussian: Zero-shot 4D Style Transfer with Gaussian Splatting](https://mlsanity.com/paper/2410.10412/) (2024)
- [CAMS: Color-Aware Multi-Style Transfer](https://mlsanity.com/paper/2106.13920/) (2021)
- [STALP: Style Transfer with Auxiliary Limited Pairing](https://mlsanity.com/paper/2110.10501/) (2021)
- [Interpolated SelectionConv for Spherical Images and Surfaces](https://mlsanity.com/paper/2210.10123/) (2022)

## Abstract (arXiv)

Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approach, we adapt to videos the original image style transfer technique by Gatys et al. based on energy minimization. We introduce new ways of initialization and new loss functions to generate consistent and stable stylized video sequences even in cases with large motion and strong occlusion. Our second approach formulates video stylization as a learning problem. We propose a deep network architecture and training procedures that allow us to stylize arbitrary-length videos in a consistent and stable way, and nearly in real time. We show that the proposed methods clearly outperform simpler baselines both qualitatively and quantitatively. Finally, we propose a way to adapt these approaches also to 360 degree images and videos as they emerge with recent virtual reality hardware.