# Be Your Own Prada: Fashion Synthesis with Structural Coherence

arXiv: https://arxiv.org/abs/1710.07346 · HTML: https://mlsanity.com/paper/1710.07346/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2017.

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

- [More Control for Free! Image Synthesis with Semantic Diffusion Guidance](https://mlsanity.com/paper/2112.05744/) (2021)
- [HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation](https://mlsanity.com/paper/2304.04269/) (2023)
- [Talk-to-Edit: Fine-Grained Facial Editing via Dialog](https://mlsanity.com/paper/2109.04425/) (2021)
- [HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion](https://mlsanity.com/paper/2310.08579/) (2023)
- [Disentangled Cycle Consistency for Highly-realistic Virtual Try-On](https://mlsanity.com/paper/2103.09479/) (2021)
- [Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2304.02051/) (2023)
- [Toward Accurate and Realistic Outfits Visualization with Attention to Details](https://mlsanity.com/paper/2106.06593/) (2021)
- [FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion](https://mlsanity.com/paper/2301.02110/) (2023)
- [SGDiff: A Style Guided Diffusion Model for Fashion Synthesis](https://mlsanity.com/paper/2308.07605/) (2023)
- [On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition](https://mlsanity.com/paper/2106.03062/) (2021)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Cloth Interactive Transformer for Virtual Try-On](https://mlsanity.com/paper/2104.05519/) (2021)
- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [FashionTex: Controllable Virtual Try-on with Text and Texture](https://mlsanity.com/paper/2305.04451/) (2023)
- [Towards Better Adversarial Synthesis of Human Images from Text](https://mlsanity.com/paper/2107.01869/) (2021)
- [Language-based Photo Color Adjustment for Graphic Designs](https://mlsanity.com/paper/2308.03059/) (2023)
- [HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models](https://mlsanity.com/paper/2401.07450/) (2024)
- [DiffBody: Human Body Restoration by Imagining with Generative Diffusion Prior](https://mlsanity.com/paper/2404.03642/) (2024)
- [Neural 3D Clothes Retargeting from a Single Image](https://mlsanity.com/paper/2102.00062/) (2021)
- [Neural Fashion Image Captioning : Accounting for Data Diversity](https://mlsanity.com/paper/2106.12154/) (2021)
- [DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches](https://mlsanity.com/paper/2205.02070/) (2022)
- [Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2403.14828/) (2024)
- [A Survey of Multimodal Composite Editing and Retrieval](https://mlsanity.com/paper/2409.05405/) (2024)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing](https://mlsanity.com/paper/2111.12346/) (2021)
- [A Diffusion-based Method for Multi-turn Compositional Image Generation](https://mlsanity.com/paper/2304.02192/) (2023)
- [Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework](https://mlsanity.com/paper/2502.06827/) (2025)
- [Remember What You have drawn: Semantic Image Manipulation with Memory](https://mlsanity.com/paper/2107.12579/) (2021)
- [Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design](https://mlsanity.com/paper/2311.12067/) (2023)

## Abstract (arXiv)

We present a novel and effective approach for generating new clothing on a wearer through generative adversarial learning. Given an input image of a person and a sentence describing a different outfit, our model "redresses" the person as desired, while at the same time keeping the wearer and her/his pose unchanged. Generating new outfits with precise regions conforming to a language description while retaining wearer's body structure is a new challenging task. Existing generative adversarial networks are not ideal in ensuring global coherence of structure given both the input photograph and language description as conditions. We address this challenge by decomposing the complex generative process into two conditional stages. In the first stage, we generate a plausible semantic segmentation map that obeys the wearer's pose as a latent spatial arrangement. An effective spatial constraint is formulated to guide the generation of this semantic segmentation map. In the second stage, a generative model with a newly proposed compositional mapping layer is used to render the final image with precise regions and textures conditioned on this map. We extended the DeepFashion dataset [8] by collecting sentence descriptions for 79K images. We demonstrate the effectiveness of our approach through both quantitative and qualitative evaluations. A user study is also conducted. The codes and the data are available at http://mmlab.ie.cuhk. edu.hk/projects/FashionGAN/.