# Deformable Style Transfer

arXiv: https://arxiv.org/abs/2003.11038 · HTML: https://mlsanity.com/paper/2003.11038/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 30 of 31)

- [Splicing ViT Features for Semantic Appearance Transfer](https://mlsanity.com/paper/2201.00424/) (2022)
- [The Spatially-Correlative Loss for Various Image Translation Tasks](https://mlsanity.com/paper/2104.00854/) (2021)
- [JoJoGAN: One Shot Face Stylization](https://mlsanity.com/paper/2112.11641/) (2021)
- [ArtFID: Quantitative Evaluation of Neural Style Transfer](https://mlsanity.com/paper/2207.12280/) (2022)
- [Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes](https://mlsanity.com/paper/2103.17185/) (2021)
- [Image-Based CLIP-Guided Essence Transfer](https://mlsanity.com/paper/2110.12427/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models](https://mlsanity.com/paper/2302.06826/) (2023)
- [EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer](https://mlsanity.com/paper/2207.09840/) (2022)
- [SNeRF: Stylized Neural Implicit Representations for 3D Scenes](https://mlsanity.com/paper/2207.02363/) (2022)
- [Facial Attribute Transformers for Precise and Robust Makeup Transfer](https://mlsanity.com/paper/2104.02894/) (2021)
- [3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction](https://mlsanity.com/paper/2103.08204/) (2021)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [Deformable One-shot Face Stylization via DINO Semantic Guidance](https://mlsanity.com/paper/2403.00459/) (2024)
- [Texture Reformer: Towards Fast and Universal Interactive Texture Transfer](https://mlsanity.com/paper/2112.02788/) (2021)
- [Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation](https://mlsanity.com/paper/2203.12835/) (2022)
- [DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields](https://mlsanity.com/paper/2309.04410/) (2023)
- [SPAC-Net: Synthetic Pose-aware Animal ControlNet for Enhanced Pose Estimation](https://mlsanity.com/paper/2305.17845/) (2023)
- [Instruct-Video2Avatar: Video-to-Avatar Generation with Instructions](https://mlsanity.com/paper/2306.02903/) (2023)
- [DiffArtist: Towards Structure and Appearance Controllable Image Stylization](https://mlsanity.com/paper/2407.15842/) (2024)
- [DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer](https://mlsanity.com/paper/2307.04157/) (2023)
- [Disentangling Structure and Appearance in ViT Feature Space](https://mlsanity.com/paper/2311.12193/) (2023)
- [AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation](https://mlsanity.com/paper/2305.19245/) (2023)
- [FaceRefiner: High-Fidelity Facial Texture Refinement with Differentiable Rendering-based Style Transfer](https://mlsanity.com/paper/2601.04520/) (2026)
- [Realtime Data-Efficient Portrait Stylization Based On Geometric Alignment](https://mlsanity.com/paper/2211.15549/) (2022)
- [DSI2I: Dense Style for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2212.13253/) (2022)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [UStyle: Waterbody Style Transfer of Underwater Scenes by Depth-Guided Feature Synthesis](https://mlsanity.com/paper/2503.11893/) (2025)
- [Non-Parametric Neural Style Transfer](https://mlsanity.com/paper/2108.12847/) (2021)
- [One-Shot Stylization for Full-Body Human Images](https://mlsanity.com/paper/2304.06917/) (2023)

## Abstract (arXiv)

Both geometry and texture are fundamental aspects of visual style. Existing style transfer methods, however, primarily focus on texture, almost entirely ignoring geometry. We propose deformable style transfer (DST), an optimization-based approach that jointly stylizes the texture and geometry of a content image to better match a style image. Unlike previous geometry-aware stylization methods, our approach is neither restricted to a particular domain (such as human faces), nor does it require training sets of matching style/content pairs. We demonstrate our method on a diverse set of content and style images including portraits, animals, objects, scenes, and paintings. Code has been made publicly available at https://github.com/sunniesuhyoung/DST.