# Consistent Style Transfer

arXiv: https://arxiv.org/abs/2201.02233 · HTML: https://mlsanity.com/paper/2201.02233/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [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)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (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)
- [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)
- [Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration](https://mlsanity.com/paper/1805.03857/) (2018)
- [Fast Patch-based Style Transfer of Arbitrary Style](https://mlsanity.com/paper/1612.04337/) (2016)
- [StyleBank: An Explicit Representation for Neural Image Style Transfer](https://mlsanity.com/paper/1703.09210/) (2017)
- [Arbitrary Video Style Transfer via Multi-Channel Correlation](https://mlsanity.com/paper/2009.08003/) (2020)
- [Dynamic Instance Normalization for Arbitrary Style Transfer](https://mlsanity.com/paper/1911.06953/) (2019)
- [Arbitrary Style Transfer with Deep Feature Reshuffle](https://mlsanity.com/paper/1805.04103/) (2018)
- [Arbitrary Style Transfer via Multi-Adaptation Network](https://mlsanity.com/paper/2005.13219/) (2020)
- [HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms](https://mlsanity.com/paper/2011.11731/) (2020)
- [Multi-style Generative Network for Real-time Transfer](https://mlsanity.com/paper/1703.06953/) (2017)
- [Diversified Texture Synthesis with Feed-forward Networks](https://mlsanity.com/paper/1703.01664/) (2017)
- [Attention-aware Multi-stroke Style Transfer](https://mlsanity.com/paper/1901.05127/) (2019)
- [Manifold Alignment for Semantically Aligned Style Transfer](https://mlsanity.com/paper/2005.10777/) (2020)
- [Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer](https://mlsanity.com/paper/1612.01895/) (2016)

## Built on by (most cited first, 13 of 13)

- [ArtFID: Quantitative Evaluation of Neural Style Transfer](https://mlsanity.com/paper/2207.12280/) (2022)
- [AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks](https://mlsanity.com/paper/2307.09724/) (2023)
- [Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers](https://mlsanity.com/paper/2304.11335/) (2023)
- [Learning to Evaluate the Artness of AI-generated Images](https://mlsanity.com/paper/2305.04923/) (2023)
- [HyperNST: Hyper-Networks for Neural Style Transfer](https://mlsanity.com/paper/2208.04807/) (2022)
- [PARASOL: Parametric Style Control for Diffusion Image Synthesis](https://mlsanity.com/paper/2303.06464/) (2023)
- [DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer](https://mlsanity.com/paper/2307.04157/) (2023)
- [NeAT: Neural Artistic Tracing for Beautiful Style Transfer](https://mlsanity.com/paper/2304.05139/) (2023)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer](https://mlsanity.com/paper/2304.05755/) (2023)
- [Neural Style Transfer for Computer Games](https://mlsanity.com/paper/2311.14617/) (2023)
- [Leveraging Diffusion Models for Stylization using Multiple Style Images](https://mlsanity.com/paper/2508.12784/) (2025)
- [PQDAST: Depth-Aware Arbitrary Style Transfer for Games via Perceptual Quality-Guided Distillation](https://mlsanity.com/paper/2502.16996/) (2025)

## Abstract (arXiv)

Recently, attentional arbitrary style transfer methods have been proposed to achieve fine-grained results, which manipulates the point-wise similarity between content and style features for stylization. However, the attention mechanism based on feature points ignores the feature multi-manifold distribution, where each feature manifold corresponds to a semantic region in the image. Consequently, a uniform content semantic region is rendered by highly different patterns from various style semantic regions, producing inconsistent stylization results with visual artifacts. We proposed the progressive attentional manifold alignment (PAMA) to alleviate this problem, which repeatedly applies attention operations and space-aware interpolations. The attention operation rearranges style features dynamically according to the spatial distribution of content features. This makes the content and style manifolds correspond on the feature map. Then the space-aware interpolation adaptively interpolates between the corresponding content and style manifolds to increase their similarity. By gradually aligning the content manifolds to style manifolds, the proposed PAMA achieves state-of-the-art performance while avoiding the inconsistency of semantic regions. Codes are available at https://github.com/computer-vision2022/PAMA.