# Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On

arXiv: https://arxiv.org/abs/2007.02721 · HTML: https://mlsanity.com/paper/2007.02721/

## Key facts

- Cited by 77 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 30 of 77)

- [StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On](https://mlsanity.com/paper/2312.01725/) (2023)
- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On](https://mlsanity.com/paper/2305.13501/) (2023)
- [OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://mlsanity.com/paper/2403.01779/) (2024)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)
- [TryOnDiffusion: A Tale of Two UNets](https://mlsanity.com/paper/2306.08276/) (2023)
- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)
- [GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning](https://mlsanity.com/paper/2303.13756/) (2023)
- [Improving Diffusion Models for Authentic Virtual Try-on in the Wild](https://mlsanity.com/paper/2403.05139/) (2024)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Style-Based Global Appearance Flow for Virtual Try-On](https://mlsanity.com/paper/2204.01046/) (2022)
- [Single Stage Virtual Try-on via Deformable Attention Flows](https://mlsanity.com/paper/2207.09161/) (2022)
- [Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment](https://mlsanity.com/paper/2403.12965/) (2024)
- [FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on](https://mlsanity.com/paper/2411.10499/) (2024)
- [Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2404.01089/) (2024)
- [Point-Based Modeling of Human Clothing](https://mlsanity.com/paper/2104.08230/) (2021)
- [BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training](https://mlsanity.com/paper/2408.06047/) (2024)
- [Toward Accurate and Realistic Outfits Visualization with Attention to Details](https://mlsanity.com/paper/2106.06593/) (2021)
- [Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos](https://mlsanity.com/paper/2404.17571/) (2024)
- [M3D-VTON: A Monocular-to-3D Virtual Try-On Network](https://mlsanity.com/paper/2108.05126/) (2021)
- [C-VTON: Context-Driven Image-Based Virtual Try-On Network](https://mlsanity.com/paper/2212.04437/) (2022)
- [M&M VTO: Multi-Garment Virtual Try-On and Editing](https://mlsanity.com/paper/2406.04542/) (2024)
- [StableGarment: Garment-Centric Generation via Stable Diffusion](https://mlsanity.com/paper/2403.10783/) (2024)
- [ClothFormer:Taming Video Virtual Try-on in All Module](https://mlsanity.com/paper/2204.12151/) (2022)
- [GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Prompting](https://mlsanity.com/paper/2405.07472/) (2024)
- [WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models](https://mlsanity.com/paper/2407.10625/) (2024)
- [FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion](https://mlsanity.com/paper/2301.02110/) (2023)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [WAS-VTON: Warping Architecture Search for Virtual Try-on Network](https://mlsanity.com/paper/2108.00386/) (2021)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)

## Abstract (arXiv)

The 2D virtual try-on task has recently attracted a great interest from the research community, for its direct potential applications in online shopping as well as for its inherent and non-addressed scientific challenges. This task requires fitting an in-shop cloth image on the image of a person, which is highly challenging because it involves cloth warping, image compositing, and synthesizing. Casting virtual try-on into a supervised task faces a difficulty: available datasets are composed of pairs of pictures (cloth, person wearing the cloth). Thus, we have no access to ground-truth when the cloth on the person changes. State-of-the-art models solve this by masking the cloth information on the person with both a human parser and a pose estimator. Then, image synthesis modules are trained to reconstruct the person image from the masked person image and the cloth image. This procedure has several caveats: firstly, human parsers are prone to errors; secondly, it is a costly pre-processing step, which also has to be applied at inference time; finally, it makes the task harder than it is since the mask covers information that should be kept such as hands or accessories. In this paper, we propose a novel student-teacher paradigm where the teacher is trained in the standard way (reconstruction) before guiding the student to focus on the initial task (changing the cloth). The student additionally learns from an adversarial loss, which pushes it to follow the distribution of the real images. Consequently, the student exploits information that is masked to the teacher. A student trained without the adversarial loss would not use this information. Also, getting rid of both human parser and pose estimator at inference time allows obtaining a real-time virtual try-on.