# FEAT: Fashion Editing and Try-On from Any Design

arXiv: https://arxiv.org/abs/2605.02393 · HTML: https://mlsanity.com/paper/2605.02393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 21 of 21)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16322/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation](https://mlsanity.com/paper/2404.02733/) (2024)
- [GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation](https://mlsanity.com/paper/2401.04092/) (2024)
- [Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2304.02051/) (2023)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [Laplacian-Steered Neural Style Transfer](https://mlsanity.com/paper/1707.01253/) (2017)
- [PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns](https://mlsanity.com/paper/2312.04534/) (2023)
- [Diverse Text-to-Image Generation via Contrastive Noise Optimization](https://mlsanity.com/paper/2510.03813/) (2025)
- [FashionTex: Controllable Virtual Try-on with Text and Texture](https://mlsanity.com/paper/2305.04451/) (2023)
- [LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing](https://mlsanity.com/paper/2507.22627/) (2025)
- [Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network](https://mlsanity.com/paper/2405.19775/) (2024)
- [Bayesian Principles Improve Prompt Learning In Vision-Language Models](https://mlsanity.com/paper/2504.14123/) (2025)
- [Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Control](https://mlsanity.com/paper/2404.15889/) (2024)
- [Controllable 3D Object Generation with Single Image Prompt](https://mlsanity.com/paper/2511.22194/) (2025)

## Abstract (arXiv)

Fashion design aims to express a designer's creative intent and to depict how garments interact with the human body. Recent methods condition on multimodal inputs to support garment editing and virtual try-on. However, existing methods still (i) confine design to garment-related images, excluding creative design sources such as artwork, abstract imagery, and natural photographs, and (ii) cannot support complete outfits, including accessories. We present FEAT (Fashion Editing And Try-On from Any Design), a method that enables editing and try-on across garments and accessories using diverse design sources. To achieve this, we introduce Disentangled Dual Injection (DDI). It takes both apparel and non-apparel design sources and selectively injects design cues via content and style disentanglement. Furthermore, we propose Orthogonal-Guided Noise Fusion (OGNF), a training-free mechanism that removes residual garments via orthogonal projection and applies region-specific noise strategies to enable virtual try-on for both garments and accessories. Extensive experiments demonstrate that FEAT achieves state-of-the-art performance in design flexibility, prompt consistency, and visual realism.