# MV-VTON: Multi-View Virtual Try-On with Diffusion Models

arXiv: https://arxiv.org/abs/2404.17364 · HTML: https://mlsanity.com/paper/2404.17364/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 40 of its 54 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 40)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Composer: Creative and Controllable Image Synthesis with Composable Conditions](https://mlsanity.com/paper/2302.09778/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [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)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)

## Built on by (most cited first, 25 of 25)

- [ImgEdit: A Unified Image Editing Dataset and Benchmark](https://mlsanity.com/paper/2505.20275/) (2025)
- [CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2407.15886/) (2024)
- [DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation](https://mlsanity.com/paper/2511.19365/) (2025)
- [CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation](https://mlsanity.com/paper/2501.11325/) (2025)
- [MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation](https://mlsanity.com/paper/2405.00448/) (2024)
- [DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a Stage-Wise Diffusion Transformer Framework](https://mlsanity.com/paper/2508.02807/) (2025)
- [VTON 360: High-Fidelity Virtual Try-On from Any Viewing Direction](https://mlsanity.com/paper/2503.12165/) (2025)
- [DreamFit: Garment-Centric Human Generation via a Lightweight Anything-Dressing Encoder](https://mlsanity.com/paper/2412.17644/) (2024)
- [JCo-MVTON: Jointly Controllable Multi-Modal Diffusion Transformer for Mask-Free Virtual Try-on](https://mlsanity.com/paper/2508.17614/) (2025)
- [MoSA: Motion-Coherent Human Video Generation via Structure-Appearance Decoupling](https://mlsanity.com/paper/2508.17404/) (2025)
- [UniFit: Towards Universal Virtual Try-on with MLLM-Guided Semantic Alignment](https://mlsanity.com/paper/2511.15831/) (2025)
- [VTONQA: A Multi-Dimensional Quality Assessment Dataset for Virtual Try-on](https://mlsanity.com/paper/2601.02945/) (2026)
- [HF-VTON: High-Fidelity Virtual Try-On via Consistent Geometric and Semantic Alignment](https://mlsanity.com/paper/2505.19638/) (2025)
- [DualFit: A Two-Stage Virtual Try-On via Warping and Synthesis](https://mlsanity.com/paper/2508.12131/) (2025)
- [OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation](https://mlsanity.com/paper/2601.22725/) (2026)
- [One Model for All: Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion](https://mlsanity.com/paper/2508.04559/) (2025)
- [DiT-VTON: Diffusion Transformer Framework for Unified Multi-Category Virtual Try-On and Virtual Try-All with Integrated Image Editing](https://mlsanity.com/paper/2510.04797/) (2025)
- [GO-MLVTON: Garment Occlusion-Aware Multi-Layer Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2601.13524/) (2026)
- [VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-On](https://mlsanity.com/paper/2603.11734/) (2026)
- [FashionComposer: Compositional Fashion Image Generation](https://mlsanity.com/paper/2412.14168/) (2024)
- [EOPose : Exemplar-based object reposing using Generalized Pose Correspondences](https://mlsanity.com/paper/2505.03394/) (2025)
- [Efficient Encoder-Free Pose Conditioning and Pose Control for Virtual Try-On](https://mlsanity.com/paper/2509.20343/) (2025)
- [MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data](https://mlsanity.com/paper/2603.08147/) (2026)
- [Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition](https://mlsanity.com/paper/2605.10127/) (2026)
- [BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data](https://mlsanity.com/paper/2609.04120/) (2026)

## Abstract (arXiv)

The goal of image-based virtual try-on is to generate an image of the target person naturally wearing the given clothing. However, existing methods solely focus on the frontal try-on using the frontal clothing. When the views of the clothing and person are significantly inconsistent, particularly when the person's view is non-frontal, the results are unsatisfactory. To address this challenge, we introduce Multi-View Virtual Try-ON (MV-VTON), which aims to reconstruct the dressing results from multiple views using the given clothes. Given that single-view clothes provide insufficient information for MV-VTON, we instead employ two images, i.e., the frontal and back views of the clothing, to encompass the complete view as much as possible. Moreover, we adopt diffusion models that have demonstrated superior abilities to perform our MV-VTON. In particular, we propose a view-adaptive selection method where hard-selection and soft-selection are applied to the global and local clothing feature extraction, respectively. This ensures that the clothing features are roughly fit to the person's view. Subsequently, we suggest joint attention blocks to align and fuse clothing features with person features. Additionally, we collect a MV-VTON dataset MVG, in which each person has multiple photos with diverse views and poses. Experiments show that the proposed method not only achieves state-of-the-art results on MV-VTON task using our MVG dataset, but also has superiority on frontal-view virtual try-on task using VITON-HD and DressCode datasets.