# Image-based Virtual Fitting Room

arXiv: https://arxiv.org/abs/2104.04104 · HTML: https://mlsanity.com/paper/2104.04104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Photographic Image Synthesis with Cascaded Refinement Networks](https://mlsanity.com/paper/1707.09405/) (2017)
- [Be Your Own Prada: Fashion Synthesis with Structural Coherence](https://mlsanity.com/paper/1710.07346/) (2017)
- [The Conditional Analogy GAN: Swapping Fashion Articles on People Images](https://mlsanity.com/paper/1709.04695/) (2017)
- [ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations](https://mlsanity.com/paper/1807.01394/) (2018)
- [Fashion Forward: Forecasting Visual Style in Fashion](https://mlsanity.com/paper/1705.06394/) (2017)
- [Looking at Outfit to Parse Clothing](https://mlsanity.com/paper/1703.01386/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Cost-Efficient Approach for Creating Virtual Fitting Room using Generative Adversarial Networks (GANs)](https://mlsanity.com/paper/2402.00994/) (2024)

## Abstract (arXiv)

Virtual fitting room is a challenging task yet useful feature for e-commerce platforms and fashion designers. Existing works can only detect very few types of fashion items. Besides they did poorly in changing the texture and style of the selected fashion items. In this project, we propose a novel approach to address this problem. We firstly used Mask R-CNN to find the regions of different fashion items, and secondly used Neural Style Transfer to change the style of the selected fashion items. The dataset we used is composed of images from PaperDoll dataset and annotations provided by eBay's ModaNet. We trained 8 models and our best model massively outperformed baseline models both quantitatively and qualitatively, with 68.72% mAP, 0.2% ASDR.