# The Conditional Analogy GAN: Swapping Fashion Articles on People Images

arXiv: https://arxiv.org/abs/1709.04695 · HTML: https://mlsanity.com/paper/1709.04695/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2017.

## Built on by (most cited first, 30 of 33)

- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)
- [Disentangled Cycle Consistency for Highly-realistic Virtual Try-On](https://mlsanity.com/paper/2103.09479/) (2021)
- [Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing](https://mlsanity.com/paper/2104.07021/) (2021)
- [Learning Semantic Person Image Generation by Region-Adaptive Normalization](https://mlsanity.com/paper/2104.06650/) (2021)
- [Point-Based Modeling of Human Clothing](https://mlsanity.com/paper/2104.08230/) (2021)
- [C-VTON: Context-Driven Image-Based Virtual Try-On Network](https://mlsanity.com/paper/2212.04437/) (2022)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models](https://mlsanity.com/paper/2411.18350/) (2024)
- [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)
- [Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems](https://mlsanity.com/paper/2111.15140/) (2021)
- [Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation](https://mlsanity.com/paper/2203.12835/) (2022)
- [MGT: Extending Virtual Try-Off to Multi-Garment Scenarios](https://mlsanity.com/paper/2504.13078/) (2025)
- [Per Garment Capture and Synthesis for Real-time Virtual Try-on](https://mlsanity.com/paper/2109.04654/) (2021)
- [Limb-Aware Virtual Try-On Network with Progressive Clothing Warping](https://mlsanity.com/paper/2503.14074/) (2025)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Progressive Limb-Aware Virtual Try-On](https://mlsanity.com/paper/2503.12588/) (2025)
- [An Efficient Style Virtual Try on Network for Clothing Business Industry](https://mlsanity.com/paper/2105.13183/) (2021)
- [PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer](https://mlsanity.com/paper/2111.12167/) (2021)
- [Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers](https://mlsanity.com/paper/2205.06305/) (2022)
- [HF-VTON: High-Fidelity Virtual Try-On via Consistent Geometric and Semantic Alignment](https://mlsanity.com/paper/2505.19638/) (2025)
- [Image-based Virtual Fitting Room](https://mlsanity.com/paper/2104.04104/) (2021)
- [Significance of Skeleton-based Features in Virtual Try-On](https://mlsanity.com/paper/2208.08076/) (2022)
- [Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On](https://mlsanity.com/paper/2210.00918/) (2022)
- [Hierarchical Cross-Attention Network for Virtual Try-On](https://mlsanity.com/paper/2411.15542/) (2024)
- [Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On](https://mlsanity.com/paper/2506.10468/) (2025)
- [Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments](https://mlsanity.com/paper/2506.12348/) (2025)
- [An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator](https://mlsanity.com/paper/2110.11293/) (2021)
- [Controllable Garment Transfer](https://mlsanity.com/paper/2204.01965/) (2022)

## Abstract (arXiv)

We present a novel method to solve image analogy problems : it allows to learn the relation between paired images present in training data, and then generalize and generate images that correspond to the relation, but were never seen in the training set. Therefore, we call the method Conditional Analogy Generative Adversarial Network (CAGAN), as it is based on adversarial training and employs deep convolutional neural networks. An especially interesting application of that technique is automatic swapping of clothing on fashion model photos. Our work has the following contributions. First, the definition of the end-to-end trainable CAGAN architecture, which implicitly learns segmentation masks without expensive supervised labeling data. Second, experimental results show plausible segmentation masks and often convincing swapped images, given the target article. Finally, we discuss the next steps for that technique: neural network architecture improvements and more advanced applications.