# Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)

arXiv: https://arxiv.org/abs/2111.08270 · HTML: https://mlsanity.com/paper/2111.08270/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)

## Built on by (most cited first, 4 of 4)

- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup](https://mlsanity.com/paper/2301.00965/) (2023)
- [Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals](https://mlsanity.com/paper/2505.21062/) (2025)
- [From Keypoints to Realism: A Realistic and Accurate Virtual Try-on Network from 2D Images](https://mlsanity.com/paper/2504.03807/) (2025)

## Abstract (arXiv)

Image-based virtual try-on provides the capacity to transfer a clothing item onto a photo of a given person, which is usually accomplished by warping the item to a given human pose and adjusting the warped item to the person. However, the results of real-world synthetic images (e.g., selfies) from the previous method is not realistic because of the limitations which result in the neck being misrepresented and significant changes to the style of the garment. To address these challenges, we propose a novel method to solve this unique issue, called VITON-CROP. VITON-CROP synthesizes images more robustly when integrated with random crop augmentation compared to the existing state-of-the-art virtual try-on models. In the experiments, we demonstrate that VITON-CROP is superior to VITON-HD both qualitatively and quantitatively.