# Manifold Alignment for Semantically Aligned Style Transfer

arXiv: https://arxiv.org/abs/2005.10777 · HTML: https://mlsanity.com/paper/2005.10777/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 30 of 34)

- [StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models](https://mlsanity.com/paper/2308.07863/) (2023)
- [UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models](https://mlsanity.com/paper/2402.11846/) (2024)
- [ArtFID: Quantitative Evaluation of Neural Style Transfer](https://mlsanity.com/paper/2207.12280/) (2022)
- [Learning Graph Neural Networks for Image Style Transfer](https://mlsanity.com/paper/2207.11681/) (2022)
- [WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation](https://mlsanity.com/paper/2109.14196/) (2021)
- [DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation](https://mlsanity.com/paper/2207.06124/) (2022)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate](https://mlsanity.com/paper/2304.09728/) (2023)
- [Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization](https://mlsanity.com/paper/2303.13232/) (2023)
- [Consistent Style Transfer](https://mlsanity.com/paper/2201.02233/) (2022)
- [Learning Dynamic Style Kernels for Artistic Style Transfer](https://mlsanity.com/paper/2304.00414/) (2023)
- [Painterly Image Harmonization in Dual Domains](https://mlsanity.com/paper/2212.08846/) (2022)
- [Learning to Evaluate the Artness of AI-generated Images](https://mlsanity.com/paper/2305.04923/) (2023)
- [Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer](https://mlsanity.com/paper/2304.11818/) (2023)
- [ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting](https://mlsanity.com/paper/2503.22218/) (2025)
- [Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer](https://mlsanity.com/paper/2304.04461/) (2023)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer](https://mlsanity.com/paper/2503.04119/) (2025)
- [Style Spectroscope: Improve Interpretability and Controllability through Fourier Analysis](https://mlsanity.com/paper/2208.06140/) (2022)
- [StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter](https://mlsanity.com/paper/2312.00330/) (2023)
- [Painterly Image Harmonization by Learning from Painterly Objects](https://mlsanity.com/paper/2312.10263/) (2023)
- [GT2-GS: Geometry-aware Texture Transfer for Gaussian Splatting](https://mlsanity.com/paper/2505.15208/) (2025)
- [TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer](https://mlsanity.com/paper/2309.06004/) (2023)
- [MOSAIC: Multi-Object Segmented Arbitrary Stylization Using CLIP](https://mlsanity.com/paper/2309.13716/) (2023)
- [Neural Style Transfer for Computer Games](https://mlsanity.com/paper/2311.14617/) (2023)
- [Playing Lottery Tickets in Style Transfer Models](https://mlsanity.com/paper/2203.13802/) (2022)
- [Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles](https://mlsanity.com/paper/2312.10264/) (2023)
- [Harnessing the Latent Diffusion Model for Training-Free Image Style Transfer](https://mlsanity.com/paper/2410.01366/) (2024)
- [Line Search-Based Feature Transformation for Fast, Stable, and Tunable Content-Style Control in Photorealistic Style Transfer](https://mlsanity.com/paper/2210.05996/) (2022)
- [Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer](https://mlsanity.com/paper/2309.10528/) (2023)

## Abstract (arXiv)

Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the problem by forcing the output and style images to have similar global statistics. An alternative is the assumption of local style patterns, where algorithms are designed to swap similar local features of content and style images. However, the limitation of these existing methods is that they neglect the semantic structure of the content image which may lead to corrupted content structure in the output. In this paper, we make a new assumption that image features from the same semantic region form a manifold and an image with multiple semantic regions follows a multi-manifold distribution. Based on this assumption, the style transfer problem is formulated as aligning two multi-manifold distributions and a Manifold Alignment based Style Transfer (MAST) framework is proposed. The proposed framework allows semantically similar regions between the output and the style image share similar style patterns. Moreover, the proposed manifold alignment method is flexible to allow user editing or using semantic segmentation maps as guidance for style transfer. To allow the method to be applicable to photorealistic style transfer, we propose a new adaptive weight skip connection network structure to preserve the content details. Extensive experiments verify the effectiveness of the proposed framework for both artistic and photorealistic style transfer. Code is available at https://github.com/NJUHuoJing/MAST.