# MV-TON: Memory-based Video Virtual Try-on network

arXiv: https://arxiv.org/abs/2108.07502 · HTML: https://mlsanity.com/paper/2108.07502/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 47 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 29 of 29)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://mlsanity.com/paper/1506.07285/) (2015)
- [Dense Intrinsic Appearance Flow for Human Pose Transfer](https://mlsanity.com/paper/1903.11326/) (2019)
- [Disentangled Person Image Generation](https://mlsanity.com/paper/1712.02621/) (2017)
- [Towards Multi-pose Guided Virtual Try-on Network](https://mlsanity.com/paper/1902.11026/) (2019)
- [SFNet: Learning Object-aware Semantic Correspondence](https://mlsanity.com/paper/1904.01810/) (2019)
- [Learning Dynamic Memory Networks for Object Tracking](https://mlsanity.com/paper/1803.07268/) (2018)
- [Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/1810.11610/) (2018)
- [Memory Aggregation Networks for Efficient Interactive Video Object Segmentation](https://mlsanity.com/paper/2003.13246/) (2020)
- [Guided Image-to-Image Translation with Bi-Directional Feature Transformation](https://mlsanity.com/paper/1910.11328/) (2019)
- [M2E-Try On Net: Fashion from Model to Everyone](https://mlsanity.com/paper/1811.08599/) (2018)
- [A Read-Write Memory Network for Movie Story Understanding](https://mlsanity.com/paper/1709.09345/) (2017)
- [Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection](https://mlsanity.com/paper/2003.09790/) (2020)
- [Keypoint Based Weakly Supervised Human Parsing](https://mlsanity.com/paper/1809.05285/) (2018)
- [Learning image-to-image translation using paired and unpaired training samples](https://mlsanity.com/paper/1805.03189/) (2018)

## Built on by (most cited first, 23 of 23)

- [ViViD: Video Virtual Try-on using Diffusion Models](https://mlsanity.com/paper/2405.11794/) (2024)
- [Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos](https://mlsanity.com/paper/2404.17571/) (2024)
- [ClothFormer:Taming Video Virtual Try-on in All Module](https://mlsanity.com/paper/2204.12151/) (2022)
- [WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models](https://mlsanity.com/paper/2407.10625/) (2024)
- [OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation](https://mlsanity.com/paper/2506.01801/) (2025)
- [Fashion-VDM: Video Diffusion Model for Virtual Try-On](https://mlsanity.com/paper/2411.00225/) (2024)
- [DeCo: Decoupled Human-Centered Diffusion Video Editing with Motion Consistency](https://mlsanity.com/paper/2408.07481/) (2024)
- [VITON-DiT: Learning In-the-Wild Video Try-On from Human Dance Videos via Diffusion Transformers](https://mlsanity.com/paper/2405.18326/) (2024)
- [Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction](https://mlsanity.com/paper/2505.16980/) (2025)
- [Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism](https://mlsanity.com/paper/2412.09822/) (2024)
- [DM-VTON: Distilled Mobile Real-time Virtual Try-On](https://mlsanity.com/paper/2308.13798/) (2023)
- [Long-tailed Recognition by Learning from Latent Categories](https://mlsanity.com/paper/2206.01010/) (2022)
- [3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models](https://mlsanity.com/paper/2504.17414/) (2025)
- [Video Virtual Try-on with Conditional Diffusion Transformer Inpainter](https://mlsanity.com/paper/2506.21270/) (2025)
- [TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On](https://mlsanity.com/paper/2604.27958/) (2026)
- [SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization](https://mlsanity.com/paper/2311.16828/) (2023)
- [Dress&Dance: Dress up and Dance as You Like It - Technical Preview](https://mlsanity.com/paper/2508.21070/) (2025)
- [FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery](https://mlsanity.com/paper/2310.00106/) (2023)
- [DI-Net : Decomposed Implicit Garment Transfer Network for Digital Clothed 3D Human](https://mlsanity.com/paper/2311.16818/) (2023)
- [Shape-Guided Clothing Warping for Virtual Try-On](https://mlsanity.com/paper/2504.15232/) (2025)
- [ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On](https://mlsanity.com/paper/2506.05858/) (2025)
- [Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image -- Technical Preview](https://mlsanity.com/paper/2509.04450/) (2025)
- [BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data](https://mlsanity.com/paper/2609.04120/) (2026)

## Abstract (arXiv)

With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress. However, limited work has explored the task of video-based virtual try-on while it is important in real-world applications. Most existing video-based virtual try-on methods usually require clothing templates and they can only generate blurred and low-resolution results. To address these challenges, we propose a Memory-based Video virtual Try-On Network (MV-TON), which seamlessly transfers desired clothes to a target person without using any clothing templates and generates high-resolution realistic videos. Specifically, MV-TON consists of two modules: 1) a try-on module that transfers the desired clothes from model images to frame images by pose alignment and region-wise replacing of pixels; 2) a memory refinement module that learns to embed the existing generated frames into the latent space as external memory for the following frame generation. Experimental results show the effectiveness of our method in the video virtual try-on task and its superiority over other existing methods.