# ArtFID: Quantitative Evaluation of Neural Style Transfer

arXiv: https://arxiv.org/abs/2207.12280 · HTML: https://mlsanity.com/paper/2207.12280/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 88 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 30 of 53)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [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)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [Are GANs Created Equal? A Large-Scale Study](https://mlsanity.com/paper/1711.10337/) (2017)
- [Photographic Image Synthesis with Cascaded Refinement Networks](https://mlsanity.com/paper/1707.09405/) (2017)
- [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://mlsanity.com/paper/1706.08500/) (2017)
- [CLIPstyler: Image Style Transfer with a Single Text Condition](https://mlsanity.com/paper/2112.00374/) (2021)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [StyTr2: Image Style Transfer with Transformers](https://mlsanity.com/paper/2105.14576/) (2021)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019)
- [Effectively Unbiased FID and Inception Score and where to find them](https://mlsanity.com/paper/1911.07023/) (2019)
- [Demystifying Neural Style Transfer](https://mlsanity.com/paper/1701.01036/) (2017)
- [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)
- [ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows](https://mlsanity.com/paper/2103.16877/) (2021)
- [Coherent Online Video Style Transfer](https://mlsanity.com/paper/1703.09211/) (2017)
- [Arbitrary Style Transfer via Multi-Adaptation Network](https://mlsanity.com/paper/2005.13219/) (2020)

## Built on by (most cited first, 30 of 49)

- [Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer](https://mlsanity.com/paper/2312.09008/) (2023)
- [StyleMaster: Stylize Your Video with Artistic Generation and Translation](https://mlsanity.com/paper/2412.07744/) (2024)
- [StylizedGS: Controllable Stylization for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.05220/) (2024)
- [StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer](https://mlsanity.com/paper/2501.11319/) (2025)
- [QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity](https://mlsanity.com/paper/2212.10431/) (2022)
- [Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate](https://mlsanity.com/paper/2304.09728/) (2023)
- [UniVST: A Unified Framework for Training-free Localized Video Style Transfer](https://mlsanity.com/paper/2410.20084/) (2024)
- [One-Shot Structure-Aware Stylized Image Synthesis](https://mlsanity.com/paper/2402.17275/) (2024)
- [Learning to Evaluate the Artness of AI-generated Images](https://mlsanity.com/paper/2305.04923/) (2023)
- [O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing](https://mlsanity.com/paper/2509.01596/) (2025)
- [Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer](https://mlsanity.com/paper/2304.11818/) (2023)
- [AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2503.07307/) (2025)
- [VectorPainter: Advanced Stylized Vector Graphics Synthesis Using Stroke-Style Priors](https://mlsanity.com/paper/2405.02962/) (2024)
- [ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization](https://mlsanity.com/paper/2308.12452/) (2023)
- [FreeViS: Training-free Video Stylization with Inconsistent References](https://mlsanity.com/paper/2510.01686/) (2025)
- [FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion](https://mlsanity.com/paper/2403.03463/) (2024)
- [Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder](https://mlsanity.com/paper/2407.05552/) (2024)
- [Mamba-ST: State Space Model for Efficient Style Transfer](https://mlsanity.com/paper/2409.10385/) (2024)
- [Balanced Image Stylization with Style Matching Score](https://mlsanity.com/paper/2503.07601/) (2025)
- [SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer](https://mlsanity.com/paper/2503.15934/) (2025)
- [MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping](https://mlsanity.com/paper/2604.08364/) (2026)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles](https://mlsanity.com/paper/2505.21060/) (2025)
- [AIComposer: Any Style and Content Image Composition via Feature Integration](https://mlsanity.com/paper/2507.20721/) (2025)
- [WP-CLIP: Leveraging CLIP to Predict W"olfflin's Principles in Visual Art](https://mlsanity.com/paper/2508.12668/) (2025)
- [Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting](https://mlsanity.com/paper/2509.26455/) (2025)
- [StyleDecoupler: Generalizable Artistic Style Disentanglement](https://mlsanity.com/paper/2601.17697/) (2026)
- [StyleRWKV: High-Quality and High-Efficiency Style Transfer with RWKV-like Architecture](https://mlsanity.com/paper/2412.19535/) (2024)
- [Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging](https://mlsanity.com/paper/2503.07363/) (2025)
- [StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References](https://mlsanity.com/paper/2603.10354/) (2026)

## Abstract (arXiv)

The field of neural style transfer has experienced a surge of research exploring different avenues ranging from optimization-based approaches and feed-forward models to meta-learning methods. The developed techniques have not just progressed the field of style transfer, but also led to breakthroughs in other areas of computer vision, such as all of visual synthesis. However, whereas quantitative evaluation and benchmarking have become pillars of computer vision research, the reproducible, quantitative assessment of style transfer models is still lacking. Even in comparison to other fields of visual synthesis, where widely used metrics exist, the quantitative evaluation of style transfer is still lagging behind. To support the automatic comparison of different style transfer approaches and to study their respective strengths and weaknesses, the field would greatly benefit from a quantitative measurement of stylization performance. Therefore, we propose a method to complement the currently mostly qualitative evaluation schemes. We provide extensive evaluations and a large-scale user study to show that the proposed metric strongly coincides with human judgment.