# ClothFormer:Taming Video Virtual Try-on in All Module

arXiv: https://arxiv.org/abs/2204.12151 · HTML: https://mlsanity.com/paper/2204.12151/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 49 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [End-to-End Video Instance Segmentation with Transformers](https://mlsanity.com/paper/2011.14503/) (2020)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [Video-to-Video Synthesis](https://mlsanity.com/paper/1808.06601/) (2018)
- [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)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [Few-shot Video-to-Video Synthesis](https://mlsanity.com/paper/1910.12713/) (2019)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Learning Joint Spatial-Temporal Transformations for Video Inpainting](https://mlsanity.com/paper/2007.10247/) (2020)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)
- [Recurrent Back-Projection Network for Video Super-Resolution](https://mlsanity.com/paper/1903.10128/) (2019)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [BCNet: Learning Body and Cloth Shape from A Single Image](https://mlsanity.com/paper/2004.00214/) (2020)
- [Deep Flow-Guided Video Inpainting](https://mlsanity.com/paper/1905.02884/) (2019)
- [Deep Video Inpainting](https://mlsanity.com/paper/1905.01639/) (2019)
- [Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN](https://mlsanity.com/paper/1904.10247/) (2019)

## Built on by (most cited first, 26 of 26)

- [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)
- [CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation](https://mlsanity.com/paper/2501.11325/) (2025)
- [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)
- [MagicTryOn: Harnessing Diffusion Transformer for Garment-Preserving Video Virtual Try-on](https://mlsanity.com/paper/2505.21325/) (2025)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [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)
- [Eevee: Towards Close-up High-resolution Video-based Virtual Try-on](https://mlsanity.com/paper/2511.18957/) (2025)
- [GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling](https://mlsanity.com/paper/2210.14145/) (2022)
- [A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping](https://mlsanity.com/paper/2311.02700/) (2023)
- [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)
- [Once Is Enough: Lightweight DiT-Based Video Virtual Try-On via One-Time Garment Appearance Injection](https://mlsanity.com/paper/2510.07654/) (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)
- [Dress&Dance: Dress up and Dance as You Like It - Technical Preview](https://mlsanity.com/paper/2508.21070/) (2025)
- [SizeGAN: Improving Size Representation in Clothing Catalogs](https://mlsanity.com/paper/2211.02892/) (2022)
- [FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery](https://mlsanity.com/paper/2310.00106/) (2023)
- [ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On](https://mlsanity.com/paper/2506.05858/) (2025)
- [Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning](https://mlsanity.com/paper/2211.06688/) (2022)
- [High-Fidelity Virtual Try-on with Large-Scale Unpaired Learning](https://mlsanity.com/paper/2411.01593/) (2024)
- [Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image -- Technical Preview](https://mlsanity.com/paper/2509.04450/) (2025)
- [Neural Clothing Tryer: Customized Virtual Try-On via Semantic Enhancement and Controlling Diffusion Model](https://mlsanity.com/paper/2601.22838/) (2026)
- [FlowVVTON: Flow-Guided Mask-Free Video Virtual Try-On](https://mlsanity.com/paper/2608.30450/) (2026)

## Abstract (arXiv)

The task of video virtual try-on aims to fit the target clothes to a person in the video with spatio-temporal consistency. Despite tremendous progress of image virtual try-on, they lead to inconsistency between frames when applied to videos. Limited work also explored the task of video-based virtual try-on but failed to produce visually pleasing and temporally coherent results. Moreover, there are two other key challenges: 1) how to generate accurate warping when occlusions appear in the clothing region; 2) how to generate clothes and non-target body parts (e.g. arms, neck) in harmony with the complicated background; To address them, we propose a novel video virtual try-on framework, ClothFormer, which successfully synthesizes realistic, harmonious, and spatio-temporal consistent results in complicated environment. In particular, ClothFormer involves three major modules. First, a two-stage anti-occlusion warping module that predicts an accurate dense flow mapping between the body regions and the clothing regions. Second, an appearance-flow tracking module utilizes ridge regression and optical flow correction to smooth the dense flow sequence and generate a temporally smooth warped clothing sequence. Third, a dual-stream transformer extracts and fuses clothing textures, person features, and environment information to generate realistic try-on videos. Through rigorous experiments, we demonstrate that our method highly surpasses the baselines in terms of synthesized video quality both qualitatively and quantitatively.