{"id":6108,"url":"https://github.com/minar09/awesome-virtual-try-on","name":"awesome-virtual-try-on","description":"A curated list of awesome research papers, projects, code, dataset, workshops etc. related to virtual try-on.","projects_count":308,"last_synced_at":"2026-10-09T17:00:21.643Z","repository":{"id":39752286,"uuid":"273734636","full_name":"minar09/awesome-virtual-try-on","owner":"minar09","description":"A curated list of awesome research papers, projects, code, dataset, workshops etc. related to virtual 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(2D) Virtual Try-on","3D Virtual Try-on","Pose-Guided Human Synthesis","Related Conference Workshops","In-the-wild Virtual Try-on","Datasets for Virtual Try-on","Mix-and-match Virtual Try-on","Multi-Pose Guided Virtual Try-on","Video Virtual Try-on","Non-clothing Virtual Try-on","Demos","Related Repositories","Prompt-based Virtual Try-on","Video-to-Image Virtual Try-on"],"sub_categories":[],"readme":"# A Curated List of Awesome Virtual Try-on (VTON) Research! \u003cimg src='awesome.png' width='37'\u003e\nA curated list of awesome research papers, projects, code, datasets, workshops, etc. related to virtual try-on (VTON).\n\n- [Prompt-based Virtual Try-on](#Prompt-based-Virtual-Try-on)\n- [Image-based (2D) Virtual Try-on](#Image-based-2D-Virtual-Try-on)\n- [3D Virtual Try-on](#3D-virtual-try-on)\n- [Mix-and-match Virtual Try-On](#Mix-and-match-Virtual-Try-on)\n- [In-the-wild Virtual Try-On](#In-the-wild-Virtual-Try-on)\n- [Multi-Pose Guided Virtual Try-on](#Multi-Pose-Guided-Virtual-Try-on)\n- [Video Virtual Try-on](#Video-Virtual-Try-on)\n- [Non-clothing Virtual Try-on](#non-clothing-virtual-try-on)\n- [Pose-Guided Human Synthesis](#pose-guided-human-synthesis)\n- [Datasets for Virtual Try-on](#Datasets-for-Virtual-Try-on)\n- [Safety and Moderation for Virtual Try-on](#Safety-and-Moderation-for-Virtual-Try-on)\n- [Related Conference Workshops](#Related-Conference-Workshops)\n- [Demos](#Demos)\n- [Related Repositories](#Related-Repositories)\n\n\n## Prompt-based Virtual Try-on\n  #### ControlNet\n  - [ControlNet](https://github.com/lllyasviel/ControlNet) - Hint: Use the clothing image as the image input and provide the human description in the text prompt or vice versa.\n  - [EditAnything](https://github.com/sail-sg/EditAnything) - Hint: Use a reference fashion image as input and provide your desired changes in the text prompt.\n\n  #### Stable-Diffusion\n  - [Stable-DreamFusion](https://github.com/ashawkey/stable-dreamfusion) - Hint: Use the Image-conditioned 3D Generation option to edit your fashion images.\n  - [ThreeStudio](https://github.com/threestudio-project/threestudio) - Hint: Use different models for generating output from the image-conditioned text prompts for fashion image editing.\n\n\n## Image-based (2D) Virtual Try-on\n\n  #### CVPR 2026\n   - PG-VTON: Single-Pass Training-Free Virtual Try-On via Patch-Guided Reference Alignment - [Paper](https://openaccess.thecvf.com/content/CVPR2026/html/Zhao_PG-VTON_Single-Pass_Training-Free_Virtual_Try-On_via_Patch-Guided_Reference_Alignment_CVPR_2026_paper.html)\n   - Mobile-VTON: High-Fidelity On-Device Virtual Try-On - [Project/Paper](https://zhenchenwan.github.io/Mobile-VTON/)\n   - MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On - [Paper](https://openaccess.thecvf.com/content/CVPR2026/papers/Han_MOFA-VTON_More_Fashion_Possibilities_with_Fine-Grained_Adaptations_in_Virtual_Try-On_CVPR_2026_paper.pdf)\n   - Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision - [Project/Paper](https://hyunsoocha.github.io/vanast/)\n   - Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories - [Project](https://artmesciencelab.github.io/Garments2Look/)\n\n  #### ECCV 2026\n  - Layering Virtual Try-On - [Paper](https://doi.org/10.1007/978-3-032-37255-0_21)\n  - Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off - [Paper](https://doi.org/10.1007/978-3-032-37602-2_4)\n  - WearWow: Native 2K Multi-garment Virtual Try-On via Adaptive Token Packing and Preference Alignment - [Paper](https://doi.org/10.1007/978-3-032-37032-7_34)\n\n  #### NeurIPS 2025\n  - Mitigating Occlusions in Virtual Try-On via A Simple-Yet-Effective Mask-Free Framework - [Paper](https://openreview.net/pdf?id=oh2MpJHGHW), [Code](https://github.com/du-chenghu/OccFree-VTON/), [Project/Data](https://du-chenghu.github.io/OccFree-VTON/)\n  - VTON-VLLM: Aligning Virtual Try-On Models with Human Preferences - [Paper](https://doi.org/10.52202/085713-4937)\n  - PhysDiff-VTON: Cross-Domain Physics Modeling and Trajectory Optimization for Virtual Try-On - [Paper](https://doi.org/10.52202/085713-0323)\n\n  #### CVPR 2025\n  - VTON-HandFit: Virtual Try-on for Arbitrary Hand Pose Guided by Hand Priors Embedding - [Project](https://vton-handfit.github.io/)\n  - Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling - [Paper](https://doi.org/10.1109/cvpr52734.2025.01978)\n  - ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On - [Paper](https://doi.org/10.1109/cvpr52734.2025.02634)\n  - Robust-MVTON: Learning Cross-Pose Feature Alignment and Fusion for Robust Multi-View Virtual Try-On - [Paper](https://doi.org/10.1109/cvpr52734.2025.01494)\n  - Leffa: Learning Flow Fields in Attention for Controllable Person Image Generation - [Paper](https://doi.org/10.1109/cvpr52734.2025.00238)\n\n  #### ICCV 2025\n  - OmniVTON: Training-Free Universal Virtual Try-On - [Paper](https://doi.org/10.1109/iccv51701.2025.01551)\n  - PromptDresser: Improving the Quality and Controllability of Virtual Try-On via Generative Textual Prompt and Prompt-Aware Mask - [Paper](https://doi.org/10.1109/iccv51701.2025.01487)\n  - All Parts Matter: A Unified Mask-Free Virtual Try-On Framework - [Paper](https://doi.org/10.1109/iccv51701.2025.01815)\n  - Learning Implicit Features with Flow-Infused Transformations for Realistic Virtual Try-On - [Paper](https://doi.org/10.1109/iccv51701.2025.01741)\n  - Any2anytryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks - [Paper](https://doi.org/10.1109/iccv51701.2025.01774)\n  - TryOn-Refiner: Conditional Rectified-Flow-Based Tryon Refiner for More Accurate Detail Reconstruction - [Paper](https://doi.org/10.1109/iccv51701.2025.01454)\n\n  #### IJCAI 2025\n  - CrossVTON: Mimicking the Logic Reasoning on Cross-Category Virtual Try-On Guided by Tri-Zone Priors - [Paper](https://www.ijcai.org/proceedings/2025/0191.pdf)\n\n  #### AAAI 2025\n  - MV-VTON: Multi-View Virtual Try-On with Diffusion Models - [Paper](https://arxiv.org/abs/2404.17364), [Code/Data](https://github.com/hywang2002/MV-VTON), [Project](https://hywang2002.github.io/MV-VTON/)\n  - DreamFit: Garment-Centric Human Generation via a Lightweight Anything-Dressing Encoder - [Paper](https://doi.org/10.1609/aaai.v39i5.32554)\n\n  #### CVPR 2024\n  - StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On - [Project](https://rlawjdghek.github.io/StableVITON/), [Code/Data/Model](https://github.com/rlawjdghek/StableVITON)\n  - CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model - [Paper](https://arxiv.org/pdf/2311.18405.pdf), [Project](https://github.com/zengjianhao/CAT-DM)\n  - Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On - [Project/Code](https://github.com/Gal4way/TPD)\n  - PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns - [Project/Code](https://github.com/GAP-LAB-CUHK-SZ/PICTURE)\n  - M\u0026M VTO: Multi-Garment Virtual Try-On and Editing - [Project](https://mmvto.github.io/)\n\n  #### ACM Multimedia 2024\n  - Shape-Guided Clothing Warping for Virtual Try-On (SCW-VTON) - [Paper](https://doi.org/10.1145/3664647.3680756)\n\n  #### IJCAI 2024\n  - FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on - [Paper](https://www.ijcai.org/proceedings/2024/0151.pdf)\n\n  #### AAAI 2024\n  - Towards Squeezing-Averse Virtual Try-On via Sequential Deformation - [Paper](https://arxiv.org/abs/2312.15861), [Code](https://github.com/SHShim0513/SD-VITON)\n  \n  #### WACV 2024\n  - Wearing the Same Outfit in Different Ways -- A Controllable Virtual Try-on Method - [Paper](https://arxiv.org/abs/2211.16989)\n  - GC-VTON: Predicting Globally Consistent and Occlusion Aware Local Flows with Neighborhood Integrity Preservation for Virtual Try-on - [Paper](https://openaccess.thecvf.com/content/WACV2024/papers/Rawal_GC-VTON_Predicting_Globally_Consistent_and_Occlusion_Aware_Local_Flows_With_WACV_2024_paper.pdf)\n\n  #### ICCV 2023\n  - Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing - [Paper](https://arxiv.org/abs/2304.02051), [Code](https://github.com/aimagelab/multimodal-garment-designer)\n  - Size Does Matter: Size-aware Virtual Try-on via Clothing-oriented Transformation Try-on Network - [Paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_Size_Does_Matter_Size-aware_Virtual_Try-on_via_Clothing-oriented_Transformation_Try-on_ICCV_2023_paper.pdf), [Code](https://github.com/cotton6/COTTON-size-does-matter)\n  - Virtual Try-On with Pose-Garment Keypoints Guided Inpainting - [Paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Li_Virtual_Try-On_with_Pose-Garment_Keypoints_Guided_Inpainting_ICCV_2023_paper.pdf), [Code](https://github.com/lizhi-ntu/KGI)\n  \n  #### CVPR 2023\n  - GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning - [Paper](https://arxiv.org/pdf/2303.13756.pdf), [Code](https://github.com/xiezhy6/GP-VTON), [Project](https://github.com/xiezhy6/GP-VTON)\n  - Linking Garment with Person via Semantically Associated Landmarks for Virtual Try-On - [Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_Linking_Garment_With_Person_via_Semantically_Associated_Landmarks_for_Virtual_CVPR_2023_paper.pdf)\n  - TryOnDiffusion: A Tale of Two UNets - [Paper](https://arxiv.org/pdf/2306.08276.pdf), [Project](https://tryondiffusion.github.io/)\n  \n  #### NeurIPS 2022\n  - Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning - [Paper](https://arxiv.org/pdf/2211.14052.pdf), [Project](https://neurips.cc/virtual/2022/poster/54642)\n  \n  #### WACV 2022\n  - C-VTON: Context-Driven Image-Based Virtual Try-On Network - [Paper](https://openaccess.thecvf.com/content/WACV2022/papers/Fele_C-VTON_Context-Driven_Image-Based_Virtual_Try-On_Network_WACV_2022_paper.pdf), [Code/Model/Data](https://github.com/benquick123/C-VTON)\n  \n  #### ECCV 2022\n  - Dress Code: High-Resolution Multi-Category Virtual Try-On - [Paper](https://arxiv.org/pdf/2204.08532.pdf), [Code/Data](https://github.com/aimagelab/dress-code)\n  - High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions - [Paper](https://arxiv.org/abs/2206.14180), [Code/Model](https://github.com/sangyun884/HR-VITON)\n  - Single Stage Virtual Try-on via Deformable Attention Flows - [Paper](https://arxiv.org/abs/2207.09161), [Code/Model](https://github.com/OFA-Sys/DAFlow)\n  \n  #### CVPR 2022\n  - Full-Range Virtual Try-On with Recurrent Tri-Level Transform - [Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Full-Range_Virtual_Try-On_With_Recurrent_Tri-Level_Transform_CVPR_2022_paper.pdf), [Project](https://lzqhardworker.github.io/RT-VTON/)\n  - Style-Based Global Appearance Flow for Virtual Try-On - [Paper](https://arxiv.org/abs/2204.01046), [Code/Model](https://github.com/SenHe/Flow-Style-VTON)\n  - Weakly Supervised High-Fidelity Clothing Model Generation - [Paper](https://arxiv.org/pdf/2112.07200.pdf), [Code/Model](https://github.com/RuiLiFeng/Deep-Generative-Projection)\n  - Dressing in the Wild by Watching Dance Videos - [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Dressing_in_the_Wild_by_Watching_Dance_Videos_CVPR_2022_paper.html), [Project](https://awesome-wflow.github.io/)\n  \n  #### CVPRW 2022\n  - Dual-Branch Collaborative Transformer for Virtual Try-On - [Paper](https://openaccess.thecvf.com/content/CVPR2022W/CVFAD/papers/Fenocchi_Dual-Branch_Collaborative_Transformer_for_Virtual_Try-On_CVPRW_2022_paper.pdf)\n  - Dress Code: High-Resolution Multi-Category Virtual Try-On - [Paper](https://openaccess.thecvf.com/content/CVPR2022W/CVFAD/papers/Morelli_Dress_Code_High-Resolution_Multi-Category_Virtual_Try-On_CVPRW_2022_paper.pdf), [Code/Data](https://github.com/aimagelab/dress-code)\n  \n  #### ICCV 2021\n  - Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing - [Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Cui_Dressing_in_Order_Recurrent_Person_Image_Generation_for_Pose_Transfer_ICCV_2021_paper.pdf), [Code](https://github.com/cuiaiyu/dressing-in-order), [Colab](https://colab.research.google.com/drive/1WfeKTPtt3qtlcTlrX47J03mxUzbVvyrL?usp=sharing)\n  - ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors - [Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Chopra_ZFlow_Gated_Appearance_Flow-Based_Virtual_Try-On_With_3D_Priors_ICCV_2021_paper.pdf)\n  - FashionMirror: Co-attention Feature-remapping Virtual Try-on with Sequential Template Poses - [Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_FashionMirror_Co-Attention_Feature-Remapping_Virtual_Try-On_With_Sequential_Template_Poses_ICCV_2021_paper.pdf)\n  \n  #### CVPR 2021\n  - Parser-Free Virtual Try-on via Distilling Appearance Flows - [Paper](https://arxiv.org/pdf/2103.04559.pdf), [Code/Data/Model](https://github.com/geyuying/PF-AFN)\n  - VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization - [Paper](https://arxiv.org/pdf/2103.16874.pdf), [Code/Model](https://github.com/shadow2496/VITON-HD)\n  - Disentangled Cycle Consistency for Highly-realistic Virtual Try-On - [Paper](https://arxiv.org/pdf/2103.09479.pdf), [Code/Data/Model](https://github.com/ChongjianGE/DCTON)\n  - Toward Accurate and Realistic Outfits Visualization with Attention to Details - [Paper](https://arxiv.org/abs/2106.06593), [Demo](https://revery.ai/demo.html)\n\n  #### ACCV 2020\n  - CloTH-VTON: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON - [Paper](https://openaccess.thecvf.com/content/ACCV2020/html/Minar_CloTH-VTON_Clothing_Three-dimensional_reconstruction_for_Hybrid_image-based_Virtual_Try-ON_ACCV_2020_paper.html), [Project](https://minar09.github.io/clothvton/)\n\n  #### ECCV 2020\n  - Do Not Mask What You Do Not Need to Mask: a Parser Free Virtual Try-On - [Paper](https://arxiv.org/pdf/2007.02721.pdf)\n\n  #### CVPR 2020\n  - Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content - [Paper/Code/Data](https://github.com/switchablenorms/DeepFashion_Try_On)\n  - Image Based Virtual Try-On Network From Unpaired Data - [Paper](http://openaccess.thecvf.com/content_CVPR_2020/html/Neuberger_Image_Based_Virtual_Try-On_Network_From_Unpaired_Data_CVPR_2020_paper.html)\n  - Semantically Multi-modal Image Synthesis - [Paper/Code/Model](https://seanseattle.github.io/SMIS/)\n\n  #### CVPRW 2020\n  - CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On - [Paper/Code/Data/Model](https://minar09.github.io/cpvtonplus/)\n  - 3D Reconstruction of Clothes using a Human Body Model and its Application to Image-based Virtual Try-On - [Paper/Project](https://minar09.github.io/c3dvton/)\n\n  #### ICCV 2019\n  - VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature Preservation - [Paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Yu_VTNFP_An_Image-Based_Virtual_Try-On_Network_With_Body_and_Clothing_ICCV_2019_paper.html)\n  - ClothFlow: A Flow-Based Model for Clothed Person Generation - [Paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Han_ClothFlow_A_Flow-Based_Model_for_Clothed_Person_Generation_ICCV_2019_paper.html)\n\n  #### ICCVW 2019\n  - UVTON: UV Mapping to Consider the 3D Structure of a Human in Image-Based Virtual Try-On Network, [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/CVFAD/Kubo_UVTON_UV_Mapping_to_Consider_the_3D_Structure_of_a_ICCVW_2019_paper.html)\n  - LA-VITON: A Network for Looking-Attractive Virtual Try-On - [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/CVFAD/Lee_LA-VITON_A_Network_for_Looking-Attractive_Virtual_Try-On_ICCVW_2019_paper.html)\n  - Robust Cloth Warping via Multi-Scale Patch Adversarial Loss for Virtual Try-On Framework - [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/HBU/Ayush_Robust_Cloth_Warping_via_Multi-Scale_Patch_Adversarial_Loss_for_Virtual_ICCVW_2019_paper.html)\n  - Powering Virtual Try-On via Auxiliary Human Segmentation Learning - [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/CVFAD/Ayush_Powering_Virtual_Try-On_via_Auxiliary_Human_Segmentation_Learning_ICCVW_2019_paper.html)\n  - Generating High-Resolution Fashion Model Images Wearing Custom Outfits - [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/CVFAD/Yildirim_Generating_High-Resolution_Fashion_Model_Images_Wearing_Custom_Outfits_ICCVW_2019_paper.html)\n\n  #### ECCV 2018\n  - Toward Characteristic-Preserving Image-based Virtual Try-On Network - [Paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Bochao_Wang_Toward_Characteristic-Preserving_Image-based_ECCV_2018_paper.pdf), [Code](https://github.com/sergeywong/cp-vton)\n  - SwapNet: Garment Transfer in Single View Images - [Paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Amit_Raj_SwapNet_Garment_Transfer_ECCV_2018_paper.pdf), [Code (community contribution)](https://github.com/andrewjong/SwapNet)\n\n  #### CVPR 2018\n  - VITON: An Image-based Virtual Try-on Network - [Paper](https://arxiv.org/abs/1711.08447), [Code/Model](https://github.com/xthan/VITON)\n\n  #### Others\n  - BootComp: Controllable Human Image Generation with Personalized Multi-Garments - [Paper](https://arxiv.org/abs/2411.16801), [Project](https://yisol.github.io/BootComp/)\n  - CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models - [Paper](https://arxiv.org/pdf/2407.15886), [Code](https://github.com/Zheng-Chong/CatVTON)\n  - IMAGDressing-v1: Customizable Virtual Dressing, AAAI 2025 - [Paper](https://doi.org/10.1609/aaai.v39i7.32729), [Demo](https://sf.dictdoc.site/), [Code](https://github.com/muzishen/IMAGDressing), [Project](https://imagdressing.github.io/)\n  - Magic Clothing: Controllable Garment-Driven Image Synthesis - [Paper](https://arxiv.org/abs/2404.09512), [Code](https://github.com/ShineChen1024/MagicClothing)\n  - IDM-VTON: Improving Diffusion Models for Authentic Virtual Try-on in the Wild - [Demo](https://huggingface.co/spaces/yisol/IDM-VTON), [Paper](https://arxiv.org/abs/2403.05139), [Project](https://idm-vton.github.io/)\n  - OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on - [Code](https://github.com/levihsu/OOTDiffusion)\n  - Outfit Anyone: Ultra-high quality virtual try-on for Any Clothing and Any Person - [Project](https://humanaigc.github.io/outfit-anyone/)\n  - Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images -[Paper](https://arxiv.org/pdf/2311.16094.pdf), [Data](https://github.com/cuiaiyu/street-tryon-benchmark)\n  - FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion - [Paper](https://arxiv.org/abs/2301.02110), [Code](https://github.com/martinpernus/fice)\n  - OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup, TMM 2023 - [Paper](https://arxiv.org/abs/2301.00965), [Code](https://github.com/jychen9811/doc-vton)\n  - Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow, ACM Multimedia 2023 - [Paper](https://arxiv.org/abs/2308.06101), [Code](https://github.com/bcmi/DCI-VTON-Virtual-Try-On)\n  - LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On, ACM Multimedia 2023 - [Paper](https://arxiv.org/abs/2305.13501), [Code](https://github.com/miccunifi/ladi-vton)\n  - Limb-Aware Virtual Try-On Network with Progressive Clothing Warping, IEEE Multimedia 2023 - [Paper](https://ieeexplore.ieee.org/abstract/document/10152500)\n  - FashionTex: Controllable Virtual Try-on with Text and Texture, SIGGRAPH 2023 - [Paper](https://arxiv.org/pdf/2305.04451.pdf)\n  - DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling - [Paper](https://arxiv.org/pdf/2305.01257.pdf)\n  - PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm - [Paper](https://arxiv.org/pdf/2304.08956.pdf), [Code](https://github.com/NerdFNY/PGVTON)\n  - Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On, BMVC 2022 - [Paper](https://arxiv.org/pdf/2210.00918.pdf), [Code/Model/Data](https://github.com/hasibzunair/fifa-tryon)\n  - Probing TryOnGAN, CODS-COMAD 2022 - [Paper](https://arxiv.org/pdf/2201.01703.pdf)\n  - FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion - [Paper](https://arxiv.org/pdf/2206.11768.pdf)\n  - Spatial Transformation for Image Composition via Correspondence Learning - [Paper](https://arxiv.org/pdf/2207.02398.pdf)\n  - PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on - [Paper](https://arxiv.org/pdf/2207.13475.pdf)\n  - Significance of Skeleton-based Features in Virtual Try-On - [Paper](https://arxiv.org/pdf/2208.08076.pdf)\n  - You can try without visiting: a comprehensive survey on virtually try-on outfits, Multimedia Tools and Applications - [Paper](https://link.springer.com/article/10.1007/s11042-022-12802-6)\n  - High fidelity virtual try-on network via semantic adaptation and distributed componentization, Computational Visual Media 2022 - [Paper](https://link.springer.com/content/pdf/10.1007/s41095-021-0264-2.pdf)\n  - St-Vton: Self-Supervised Vision Transformer for Image-Based Virtual Try-On - [Paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4140115)\n  - Photo-Realistic Virtual Try-On with Enhanced Warping Module, Advances in Intelligent Systems and Computing 2022 - [Paper](https://link.springer.com/chapter/10.1007/978-981-16-5157-1_66)\n  - Virtual Try-On Using Augmented Reality - [Paper](https://link.springer.com/chapter/10.1007/978-981-19-1122-4_54)\n  - WG-VITON: Wearing-Guide Virtual Try-On for Top and Bottom Clothes - [Paper](https://arxiv.org/pdf/2205.04759.pdf)\n  - VTNCT: an image-based virtual try-on network by combining feature with pixel transformation, The Visual Computer 2022 - [Paper](https://link.springer.com/article/10.1007/s00371-022-02480-8)\n  - RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on, IJCAI-ECAI 2022 - [Paper](https://arxiv.org/pdf/2204.11258.pdf), [Code/Model/Data](https://github.com/jokerlc/RMGN-VITON)\n  - A Flow-Based Generative Network for Photo-Realistic Virtual Try-On, IEEE Access 2022 - [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9757136), [Code/Model/Data](https://github.com/gxl-groups/FVNT)\n  - An Efficient Style Virtual Try on Network for Clothing Business Industry - [Paper](https://arxiv.org/pdf/2105.13183.pdf)\n  - Cross-category Virtual Try-on Technology Research Based on PF-AFN, ICVIP 2021 - [Paper](https://dl.acm.org/doi/pdf/10.1145/3511176.3511201?casa_token=RY8suIYfg-8AAAAA:ce3F43Et11IaFISkObmvOedFz5FrH0B6QnSHPu3Ro8_GEOLKGdf8qoww1RY5V9zdGm7T9sxpND15Wcs)\n  - Virtual Try-on Network With Attribute Transformation and Local Rendering, IEEE TRANSACTIONS ON MULTIMEDIA, 2021 - [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9397349\u0026casa_token=q6ADdrsxPToAAAAA:5_PjUtYaX9d37HGetPpsvpQApUMhfCitOIOAlDpCSqW57AbRLc3Z3M0CR4vdbHsvdaEQ4qbYTkI)\n  - Transform, Warp, and Dress: A New Transformation-guided Model for Virtual Try-on, ACM Trans. Multimedia Comput. Commun. Appl. 2022 - [Paper](https://dl.acm.org/doi/pdf/10.1145/3491226?casa_token=7bTieMExNlkAAAAA%3AjGDdaeAR0HQ-M0ZO2u3olmaVjf3W_XtVgK5wPdgKiPhLP4F1yRUA28c7teapiVedba5FlPrfBTSvnC4)\n  - Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN, NeurIPS 2021. - [Paper](https://arxiv.org/abs/2111.10544)\n  - Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing - [Paper](https://arxiv.org/abs/2111.12346)\n  - PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer - [Paper](https://arxiv.org/abs/2111.12167)\n  - Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP) - [Paper](https://arxiv.org/abs/2111.08270)\n  - Per Garment Capture and Synthesis for Real-time Virtual Try-on, UIST 2021 - [Paper](https://arxiv.org/abs/2109.04654), [Project](https://sites.google.com/view/deepmannequin/home)\n  - WAS-VTON: Warping Architecture Search for Virtual Try-on Network - [Paper](https://arxiv.org/abs/2108.00386), [Code/Model](https://github.com/xiezhy6/WAS-VTON)\n  - Shape Controllable Virtual Try-on for Underwear Models - [Paper](https://arxiv.org/abs/2107.13156)\n  - Cloth Interactive Transformer for Virtual Try-On - [Paper](https://arxiv.org/abs/2104.05519), [Code/Model](https://github.com/Amazingren/CIT)\n  - CloTH-VTON+: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON, IEEE Access 2021 - [Paper](https://ieeexplore.ieee.org/document/9354778), [Project](https://minar09.github.io/clothvtonplus/)\n  - VITON-GT: An Image-based Virtual Try-On Model with Geometric Transformations, ICPR 2020 - [Paper](https://iris.unimore.it/retrieve/312578/2020_ICPR_Virtual_Try_On.pdf)\n  - TryOnGAN: Body-Aware Try-On via Layered Interpolation, SIGGRAPH 2021 - [Paper/Project](https://tryongan.github.io/tryongan/)\n  - VOGUE: Try-On by StyleGAN Interpolation Optimization - [Paper/Project](https://vogue-try-on.github.io/)\n  - Deep Virtual Try-on with Clothes Transform, ICS 2018 - [Paper](https://link.springer.com/chapter/10.1007/978-981-13-9190-3_22), [Code](https://github.com/b01902041/Deep-Virtual-Try-on-with-Clothes-Transform)\n  - NDNet: Natural Deformation Of Apparel For Better Virtual Try-On Experience, ACM SAC 2021\n  - Keypoints-Based 2D Virtual Try-on Network System, JAKO 2020 - [Paper](https://www.koreascience.or.kr/article/JAKO202010163508810.pdf)\n  - Virtual Try-On With Generative Adversarial Networks: A Taxonomical Survey - [Book chapter](https://www.igi-global.com/chapter/virtual-try-on-with-generative-adversarial-networks/260791)\n  - LGVTON: A Landmark Guided Approach to Virtual Try-On - [Paper](https://arxiv.org/abs/2004.00562v1), [Code](https://github.com/dp-isi/LGVTON)\n  - SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On, WACV 2020 - [Paper](http://openaccess.thecvf.com/content_WACV_2020/html/Jandial_SieveNet_A_Unified_Framework_for_Robust_Image-Based_Virtual_Try-On_WACV_2020_paper.html), [Code/Model (community contribution)](https://github.com/levindabhi/SieveNet)\n  - GarmentGAN: Photo-realistic Adversarial Fashion Transfer - [Paper](https://arxiv.org/abs/2003.01894)\n  - Toward Accurate and Realistic Virtual Try-on Through Shape Matching and Multiple Warps - [Paper](https://arxiv.org/abs/2003.10817)\n  - FashionFit Analysis of Mapping 3D Pose and neural body fit for custom virtual try-on, IEEE Access 2020 - [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9091008)\n  - Inpainting-Based Virtual Try-on Network for Selective Garment Transfer, IEEE Access 2019 - [Paper](https://ieeexplore.ieee.org/document/8836494)\n  - SP-VITON: shape-preserving image-based virtual try-on network, Multimedia Tools and Applications 2019 - [Paper](https://link.springer.com/article/10.1007/s11042-019-08363-w)\n  - VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss, Eurographics 2019 Posters - [Paper](https://arxiv.org/abs/1911.07926v1), [Code/Model](https://github.com/shionhonda/viton-gan)\n  - Image-Based Virtual Try-on Network with Structural Coherence, ICIP 2019 - [Paper](https://ieeexplore.ieee.org/document/8803811)\n  - End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On - [Paper](https://arxiv.org/abs/1906.01347v2)\n  - M2E-Try On Net: Fashion from Model to Everyone - [Paper](https://arxiv.org/abs/1811.08599v1)\n\n\n\n## 3D Virtual Try-on\n\n  #### CVPR 2026\n  - MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data - [Project](https://hunorlaczko.github.io/MV-Fashion/)\n\n  #### IJCV 2026\n  - GS-VTON: Controllable 3D Virtual Try-on with Gaussian Splatting - [Project](https://yukangcao.github.io/GS-VTON/), [Paper](https://arxiv.org/abs/2410.05259), [Code](https://github.com/yukangcao/GS-VTON)\n\n  #### CVPR 2025\n  - VTON 360: High-Fidelity Virtual Try-On from Any Viewing Direction - [Paper](https://doi.org/10.1109/cvpr52734.2025.02457), [Project](https://scnuhealthy.github.io/VTON360/)\n\n  #### ACM Multimedia 2024\n  - DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models - [Paper](https://doi.org/10.1145/3664647.3681391)\n\n  #### CVPR 2024\n  - DiffAvatar Simulation-Ready Garment Optimization with Differentiable Simulation - [Project](https://people.csail.mit.edu/liyifei/publication/diffavatar/), [Code](https://github.com/facebookresearch/DiffAvatar)\n  - Garment Recovery with Shape and Deformation Priors - [Project/Code](https://github.com/liren2515/GarmentRecovery)\n  - SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction - [Project](https://river-zhang.github.io/SIFU-projectpage/), [Code](https://github.com/River-Zhang/SIFU)\n\n  #### CVPR 2023\n  - DrapeNet: Garment Generation and Self-Supervised Draping - [Code](https://github.com/liren2515/DrapeNet)\n\n  #### ECCV 2022\n  - 3D Clothed Human Reconstruction in the Wild - [Code](https://github.com/hygenie1228/ClothWild_RELEASE)\n\n  #### NeurIPS 2022\n  - ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On - [Paper](https://neurips.cc/virtual/2022/poster/53336), [Project](https://mslab.es/projects/ULNeF/)\n\n  #### WACV 2022\n  - Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems - [Paper](https://openaccess.thecvf.com/content/WACV2022/papers/Majithia_Robust_3D_Garment_Digitization_From_Monocular_2D_Images_for_3D_WACV_2022_paper.pdf)\n  #### ICCV 2021\n  - M3D-VTON: A Monocular-to-3D Virtual Try-On Network - [Paper](https://arxiv.org/abs/2108.05126), [Code](https://github.com/fyviezhao/M3D-VTON)\n  #### CVPR 2021\n  - Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On - [Paper](https://arxiv.org/abs/2105.06462), [Project](http://mslab.es/projects/SelfSupervisedGarmentCollisions/), [Code](https://github.com/isantesteban/vto-garment-collisions)\n  #### ECCV 2020\n  - BCNet: Learning Body and Cloth Shape from A Single Image - [Paper](https://arxiv.org/pdf/2004.00214.pdf), [Code/Data](https://github.com/jby1993/BCNet)\n  - GAN-based Garment Generation Using Sewing Pattern Images - [Paper/Code/Model/Data](https://gamma.umd.edu/researchdirections/virtualtryon/garmentgeneration/)\n  - Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images - [Paper/Data](https://kv2000.github.io/2020/03/25/deepFashion3DRevisited/)\n  - SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing - [Paper/Code/Data](https://virtualhumans.mpi-inf.mpg.de/sizer/)\n  - CLOTH3D: Clothed 3D Humans - [Paper](https://arxiv.org/pdf/1912.02792.pdf)\n\n  #### CVPR 2020\n  - Learning to Transfer Texture from Clothing Images to 3D Humans - [Paper/Code](http://virtualhumans.mpi-inf.mpg.de/pix2surf/)\n  - TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style - [Paper/Code/Data](http://virtualhumans.mpi-inf.mpg.de/tailornet/)\n  - Learning to Dress 3D People in Generative Clothing - [Paper/Code/Data](https://cape.is.tue.mpg.de/)\n\n  #### ICCV 2019\n  - Multi-Garment Net: Learning to Dress 3D People from Images - [Paper/Code/Data](http://virtualhumans.mpi-inf.mpg.de/mgn/)\n  - 3DPeople: Modeling the Geometry of Dressed Humans - [Paper](https://arxiv.org/abs/1904.04571)\n  - GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping - [Paper/Data](https://www.epfl.ch/labs/cvlab/research/garment-simulation/garnet/)\n\n  #### ECCV 2018\n  - DeepWrinkles: Accurate and Realistic Clothing Modeling - [Paper](https://arxiv.org/abs/1808.03417)\n\n  #### CVPR 2018\n  - Video Based Reconstruction of 3D People Models - [Paper/Code/Data](http://gvv.mpi-inf.mpg.de/projects/wxu/VideoAvatar/)\n\n  #### Others\n  - DM-VTON: Distilled Mobile Real-time Virtual Try-On, ISMAR 2023 - [Paper](https://arxiv.org/abs/2308.13798), [Code](https://github.com/KiseKloset/DM-VTON)\n  - Three stages of 3D virtual try-on network with appearance flow and shape field, The Visual Computer 2023 - [Paper](https://link.springer.com/article/10.1007/s00371-023-02946-3)\n  - Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems, 2021 - [Paper](https://arxiv.org/pdf/2111.15140.pdf)\n  - REALISTIC MONOCULAR-TO-3D VIRTUAL TRY-ON VIA MULTI-SCALE CHARACTERISTICS CAPTURE, ICASSP 2022 - [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9747277\u0026casa_token=EALQVtpxonwAAAAA:GKEdQBbsji39XrEgGiSk7pJWgRB1KAH2me79_hsuaPSYTUfVn76h3Jyavxuk57CCWI6HYPRhRUk)\n  - Point-Based Modeling of Human Clothing - [Paper](https://arxiv.org/pdf/2104.08230.pdf)\n  - CloTH-VTON+: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON, IEEE Access 2021 - [Paper](https://ieeexplore.ieee.org/document/9354778), [Project](https://minar09.github.io/clothvtonplus/)\n  - Physically Based Neural Simulator for Garment Animation - [Paper](https://arxiv.org/pdf/2012.11310.pdf)\n  - GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss, IEEE T-PAMI, 2020 - [Paper](https://arxiv.org/abs/2007.10867)\n  - DeepCloth: Neural Garment Representation for Shape and Style Editing - [Paper](https://arxiv.org/abs/2011.14619)\n  - DiffuseFit: Shape-guided warping and limb-aware diffusion synthesis for occlusion-resilient and semantically consistent virtual try-on, Computer Vision and Image Understanding 2026 - [Paper](https://doi.org/10.1016/j.cviu.2026.104807)\n  - LayerDiffusion: High-Fidelity Layered Virtual Try-On via Semantic-Aware Diffusion, IEEE Transactions on Circuits and Systems for Video Technology 2026 - [Paper](https://doi.org/10.1109/TCSVT.2026.3679372)\n  - CloTH-VTON: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON, ACCV 2020 - [Paper](https://openaccess.thecvf.com/content/ACCV2020/html/Minar_CloTH-VTON_Clothing_Three-dimensional_reconstruction_for_Hybrid_image-based_Virtual_Try-ON_ACCV_2020_paper.html), [Project](https://minar09.github.io/clothvton/)\n  - Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On, ACM SCA 2020 - [Paper/Project](http://mslab.es/projects/FullyConvolutionalGraphVirtualTryOn)\n  - DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation - [Paper](https://arxiv.org/pdf/2009.02715.pdf)\n  - 3D Reconstruction of Clothes using a Human Body Model and its Application to Image-based Virtual Try-On, CVPRW 2020 - [Paper/Project](https://minar09.github.io/c3dvton/)\n  - Learning-Based Animation of Clothing for Virtual Try-On, Eurographics 2019 - [Paper/Project](http://dancasas.github.io/projects/LearningBasedVirtualTryOn/index.html), [Code](https://github.com/isantesteban/vto-learning-based-animation)\n  - Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation, SIGGRAPH Asia 2019 - [Paper/Code](http://geometry.cs.ucl.ac.uk/projects/2019/garment_authoring/)\n  - 3D Virtual Garment Modeling from RGB Images, ISMAR 2019 - [Paper](https://arxiv.org/abs/1908.00114)\n  - Deep Garment Image Matting for a Virtual Try-on System, ICCVW 2019 - [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/CVFAD/Shin_Deep_Garment_Image_Matting_for_a_Virtual_Try-on_System_ICCVW_2019_paper.html)\n  - Learning a Shared Shape Space for Multimodal Garment Design, SIGGRAPH Asia 2018 - [Paper/Code/Data](http://geometry.cs.ucl.ac.uk/projects/2018/garment_design/)\n  - Detailed Garment Recovery from a Single-View Image, ACM TOG 2018 - [Paper](https://arxiv.org/abs/1608.01250)\n  - ClothCap: Seamless 4D Clothing Capture and Retargeting, SIGGRAPH 2017 - [Paper](http://clothcap.is.tue.mpg.de/)\n  - Virtual Try-On through Image-Based Rendering, IEEE T-VCG 2013 - [Paper](https://ieeexplore.ieee.org/document/6487501)\n  - Markerless Garment Capture, ACM TOG 2008 - [Paper/Data](http://www.cs.ubc.ca/labs/imager/tr/2008/MarkerlessGarmentCapture/)\n\n\n## Mix-and-match Virtual Try-on\n  - OrthoTryOn: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation, ECCV 2026 - [Paper](https://doi.org/10.1007/978-3-032-37152-2_19)\n  - Wearing the Same Outfit in Different Ways -- A Controllable Virtual Try-on Method, WACV 2024 -[Paper](https://arxiv.org/abs/2211.16989)\n  - UMFuse: Unified Multi View Fusion for Human Editing Applications, ICCV 2023 - [Paper](https://openaccess.thecvf.com/content/ICCV2023/html/Jain_UMFuse_Unified_Multi_View_Fusion_for_Human_Editing_Applications_ICCV_2023_paper.html), [Project](https://mdsrlab.github.io/2023/08/13/UMFuse-ICCV.html)\n  - Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing, ICCV 2021 -[Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Cui_Dressing_in_Order_Recurrent_Person_Image_Generation_for_Pose_Transfer_ICCV_2021_paper.pdf), [Code](https://github.com/cuiaiyu/dressing-in-order), [Colab](https://colab.research.google.com/drive/1WfeKTPtt3qtlcTlrX47J03mxUzbVvyrL?usp=sharing)\n  - Toward Accurate and Realistic Outfits Visualization with Attention to Details, CVPR 2021 -[Paper](https://arxiv.org/pdf/2106.06593.pdf)\n  - Image Based Virtual Try-on Network from Unpaired Data, CVPR 2020 - [Paper](https://assets.amazon.science/1a/2b/7a4dd8264ce19a959559da799aff/scipub-1281.pdf), [Code](https://github.com/trinanjan12/Image-Based-Virtual-Try-on-Network-from-Unpaired-Data)\n\n## In-the-wild Virtual Try-on\n - Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images -[Paper](https://arxiv.org/pdf/2311.16094.pdf), [Data](https://github.com/cuiaiyu/street-tryon-benchmark)\n - BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training, CVPR 2025 - [Paper](https://doi.org/10.1109/cvpr52734.2025.02458)\n - Dressing in the Wild by Watching Dance Videos, CVPR 2022 - [Paper](https://arxiv.org/abs/2203.15320), [Project](https://awesome-wflow.github.io/)\n\n## Multi-Pose Guided Virtual Try-on\n\n- CF-VTON: Multi-Pose Virtual Try-on with Cross-Domain Fusion, ICASSP 2023 - [Paper](https://ieeexplore.ieee.org/abstract/document/10095176)\n- Multiple Pose Virtual Try-On Based on 3D Clothing Reconstruction, IEEE Access 2021 - [Paper](https://ieeexplore.ieee.org/document/9512041)\n- SPG-VTON: Semantic Prediction Guidance for Multi-pose Virtual Try-on - [Paper](https://arxiv.org/abs/2108.01578)\n- Down to the Last Detail: Virtual Try-on with Fine-grained Details, ACM MM 2020 - [Paper](https://dl.acm.org/doi/pdf/10.1145/3394171.3413514), [Code/Model](https://github.com/JDAI-CV/Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving), [ArXiv](https://arxiv.org/abs/1912.06324v2)\n- Towards Multi-pose Guided Virtual Try-on Network, ICCV 2019 - [Paper](https://arxiv.org/abs/1902.11026), [Code](https://github.com/thaithanhtuan/MyMGVTON)\n- FIT-ME: IMAGE-BASED VIRTUAL TRY-ON WITH ARBITRARY POSES, ICIP 2019 - [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8803681\u0026casa_token=2CL5K9pwy1IAAAAA:OTa5P-h6RWj9BdQVvkxQURR8tDy4Eg1BZynYOizMyQACnE-zL_EHu2xRzyXBOWijP_cItaO4)\n- Virtually Trying on New Clothing with Arbitrary Poses, ACM MM 2019 - [Paper](https://dl.acm.org/doi/pdf/10.1145/3343031.3350946?casa_token=w7EzejnZIaEAAAAA:KvDBsi1xYswuQuzEdJO-rsTDvysnSLYlAYi1J2st5lf8lnyotm5-umPKQupGaMEPUGxyBzijUkA9)\n- FashionOn: Semantic-guided Image-based Virtual Try-on with Detailed Human and Clothing Information, ACM MM 2019 - [Paper](https://dl.acm.org/doi/pdf/10.1145/3343031.3351075?casa_token=7y85FCo6B-QAAAAA:diZbVYmcSK13bMQ94MzrMG_-VvVG_oNFoGpI8wCBFJ_dHEzYnLBAPn2ZwbAgj_pmOWFMD6_1hOuk)\n\n  \n  \n  \n## Video Virtual Try-on\n\n- Eevee: Towards Close-up High-resolution Video-based Virtual Try-on, CVPR 2026 - [Project](https://github.com/AMAP-ML/Eevee)\n- Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction, CVPR 2025 - [Paper](https://doi.org/10.1109/cvpr52734.2025.02109)\n- Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image, NeurIPS 2025 - [Paper](https://doi.org/10.52202/085713-5188)\n- Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos, ACM Multimedia 2024 - [Paper](https://doi.org/10.1145/3664647.3680836)\n- GPD-VVTO: Preserving Garment Details in Video Virtual Try-On, ACM Multimedia 2024 - [Paper](https://doi.org/10.1145/3664647.3680701)\n- Fashion-VDM: Video Diffusion Model for Virtual Try-On, SIGGRAPH Asia 2024 - [Paper](https://doi.org/10.1145/3680528.3687623)\n- ClothFormer: Taming Video Virtual Try-on in All Module, CVPR 2022 - [Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_ClothFormer_Taming_Video_Virtual_Try-On_in_All_Module_CVPR_2022_paper.pdf), [Code](https://github.com/luxiangju-PersonAI/ClothFormer), [Project](https://cloth-former.github.io/)\n- MV-TON: Memory-based Video Virtual Try-on network, ACM MM 2021 - [Paper](https://arxiv.org/abs/2108.07502)\n- ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on, WACV 2021 Workshop - [Project/Paper/Code](https://gauravkuppa.github.io/publication/2021-01-09-shine-on-1)\n- FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On, ICCV 2019 - [Paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Dong_FW-GAN_Flow-Navigated_Warping_GAN_for_Video_Virtual_Try-On_ICCV_2019_paper.html)\n- Unsupervised Image-to-Video Clothing Transfer, ICCVW 2019 - [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/CVFAD/Pumarola_Unsupervised_Image-to-Video_Clothing_Transfer_ICCVW_2019_paper.html)\n- Garment Replacement in Monocular Video Sequences, ACM TOG 2014 - [Paper](https://dl.acm.org/doi/10.1145/2634212)\n\n## Video-to-Image Virtual Try-on\n\n- DPV-VTON : Detail-Preserving Video-based Virtual Try-On, ICMIP 2023 - [Paper](https://dl.acm.org/doi/10.1145/3599589.3599599) \n\n\n## Non-clothing Virtual Try-on\n- OmniTry: Virtual Try-On Anything without Masks - [Paper](http://arxiv.org/abs/2508.13632), [Code](https://github.com/Kunbyte-AI/OmniTry)\n- Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model, CVPR 2025 - [Paper](https://doi.org/10.1109/cvpr52734.2025.00042)\n- From Gallery to Wrist: Realistic 3D Bracelet Insertion in Videos, ICCV 2025 - [Paper](https://doi.org/10.1109/iccv51701.2025.02385)\n- ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On, NeurIPS 2025 - [Paper](https://doi.org/10.52202/085713-1088)\n- Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model, SIGGRAPH 2025 - [Code](https://github.com/Xiaojiu-z/Stable-Makeup)\n- Stable-Hair: Real-World Hair Transfer via Diffusion Model, AAAI 2025 - [Code](https://github.com/Xiaojiu-z/Stable-Hair)\n- Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers, EUROGRAPHICS 2022 - [Paper](https://arxiv.org/pdf/2205.06305.pdf)\n- ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones, ACM Multimedia 2021 - [Paper](https://arxiv.org/pdf/2108.10515.pdf)\n- Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example, CVPRW 2021 - [Paper](https://arxiv.org/pdf/2105.06407.pdf)\n- FITTINTM - Online 3D Shoe Try-on, 3DBODY.TECH 2020 - [Paper](http://www.3dbodyscanning.org/cap/papers/2020/2058revkov.pdf)\n- CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer - [Paper](https://arxiv.org/pdf/2008.10298.pdf)\n- Regularized Adversarial Training for Single-Shot Virtual Try-On, ICCVW 2019 - [Paper](http://openaccess.thecvf.com/content_ICCVW_2019/html/CVFAD/Kikuchi_Regularized_Adversarial_Training_for_Single-Shot_Virtual_Try-On_ICCVW_2019_paper.html)\n- Disentangled Makeup Transfer with Generative Adversarial Network - [Paper](https://arxiv.org/pdf/1907.01144v1.pdf)\n- PIVTONS: Pose Invariant Virtual Try-On Shoe with Conditional Image Completion, ACCV 2018 - [Paper](https://winstonhsu.info/wp-content/uploads/2018/09/chou18PIVTONS.pdf)\n- Virtual Try-on of Eyeglasses using 3D Model of the Head - [Paper](https://dl.acm.org/doi/pdf/10.1145/2087756.2087838)\n- A MIXED REALITY SYSTEM FOR VIRTUAL GLASSES TRY-ON - [Paper](https://dl.acm.org/doi/pdf/10.1145/2087756.2087816)\n- A virtual try-on system in augmented reality using RGB-D cameras for footwear personalization - [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0278612514000594)\n\n\n\n\n## Pose-Guided Human Synthesis\n- Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis, CVPR 2024 - [Code](https://github.com/YanzuoLu/CFLD)\n- IMAGPose: A Unified Conditional Framework for Pose-Guided Person Generation, NeurIPS 2024 - [Paper](https://openreview.net/forum?id=6IyYa4gETN), [Project](https://github.com/muzishen/IMAGPose)\n- Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models, ICLR 2024 - [Paper](https://openreview.net/pdf?id=rHzapPnCgT), [Project](https://github.com/tencent-ailab/PCDMs)\n- VGFlow: Visibility Guided Flow Network for Human Reposing, CVPR 2023 - [Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Jain_VGFlow_Visibility_Guided_Flow_Network_for_Human_Reposing_CVPR_2023_paper.html), [Project](https://mdsrlab.github.io/2023/03/17/VGFlow-CVPR.html)\n- Cross Attention Based Style Distribution for Controllable Person Image Synthesis, ECCV 2022 - [Paper](https://arxiv.org/pdf/2208.00712.pdf)\n- InsetGAN for Full-Body Image Generation, CVPR 2022. - [Paper](https://arxiv.org/abs/2203.07293), [Project](http://afruehstueck.github.io/insetgan)\n- Dressing in the Wild by Watching Dance Videos, CVPR 2022 - [Paper](https://arxiv.org/abs/2203.15320), [Project](https://awesome-wflow.github.io/)\n- Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN, SIGGRAPH Asia 2021 - [Paper](https://arxiv.org/abs/2109.06166), [Project](https://pose-with-style.github.io/)\n- PoNA: Pose-Guided Non-Local Attention for Human Pose Transfer, IEEE T-IP 2020 - [Paper](https://ieeexplore.ieee.org/abstract/document/9222550)\n- Pose-Guided High-Resolution Appearance Transfer via Progressive Training - [Paper](https://arxiv.org/pdf/2008.11898.pdf)\n- Recapture as You Want - [Paper](https://arxiv.org/pdf/2006.01435.pdf)\n- Generating Person Images with Appearance-aware Pose Stylizer, IJCAI 2020 - [Paper](https://arxiv.org/pdf/2007.09077.pdf), [Code](https://github.com/siyuhuang/PoseStylizer)\n- Controllable Person Image Synthesis with Attribute-Decomposed GAN, CVPR 2020 - [Paper](https://arxiv.org/pdf/2003.12267.pdf), [Code](https://github.com/menyifang/ADGAN)\n- Deep Image Spatial Transformation for Person Image Generation, CVPR 2020 - [Paper](https://arxiv.org/pdf/2003.00696v2.pdf), [Code](https://github.com/RenYurui/Global-Flow-Local-Attention)\n- Neural Pose Transfer by Spatially Adaptive Instance Normalization, CVPR 2020 - [Paper](https://arxiv.org/pdf/2003.07254v2.pdf), [Code](https://github.com/jiashunwang/Neural-Pose-Transfer)\n- Guided Image-to-Image Translation with Bi-Directional Feature Transformation, ICCV 2019 - [Paper](https://arxiv.org/pdf/1910.11328v1.pdf), [Code](https://github.com/vt-vl-lab/Guided-pix2pix)\n- Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis, ICCV 2019 - [Paper](https://arxiv.org/pdf/1909.12224.pdf), [Code](https://github.com/svip-lab/impersonator)\n- ClothFlow: A Flow-Based Model for Clothed Person Generation, ICCV 2019 - [Paper](http://openaccess.thecvf.com/content_ICCV_2019/html/Han_ClothFlow_A_Flow-Based_Model_for_Clothed_Person_Generation_ICCV_2019_paper.html)\n- Progressive Pose Attention for Person Image Generation, CVPR 2019 - [Paper](https://arxiv.org/pdf/1904.03349.pdf), [Code](https://github.com/tengteng95/Pose-Transfer)\n- Dense Intrinsic Appearance Flow for Human Pose Transfer, CVPR 2019 - [Paper](https://arxiv.org/pdf/1903.11326v1.pdf), [Code](https://github.com/ly015/intrinsic_flow)\n- Unsupervised Person Image Generation with Semantic Parsing Transformation, CVPR 2019, TPAMI 2020 - [Paper](https://arxiv.org/pdf/1904.03379.pdf), [Code](https://github.com/SijieSong/person_generation_spt)\n- Pose Guided Fashion Image Synthesis Using Deep Generative Model - [Paper](https://arxiv.org/pdf/1906.07251.pdf)\n- Synthesizing Images of Humans in Unseen Poses, CVPR 2018 - [Paper](https://arxiv.org/pdf/1804.07739.pdf), [Code](https://github.com/balakg/posewarp-cvpr2018)\n- Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis, NeurIPS 2018 - [Paper](https://arxiv.org/pdf/1810.11610.pdf)\n- Deformable GANs for Pose-based Human Image Generation, CVPR 2018 - [Paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Siarohin_Deformable_GANs_for_CVPR_2018_paper.pdf)\n- Pose-Normalized Image Generation for Person Re-identification, ECCV 2018 - [Paper](https://arxiv.org/pdf/1712.02225.pdf)\n- Disentangled Person Image Generation, CVPR 2018 - [Paper/Code/Data](https://homes.esat.kuleuven.be/~liqianma/CVPR18_DPIG/index.html)\n- A Variational U-Net for Conditional Appearance and Shape Generation, CVPR 2018 - [Paper](https://arxiv.org/pdf/1804.04694.pdf), [Code](https://github.com/CompVis/vunet)\n- Human Appearance Transfer, CVPR 2018 - [Paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zanfir_Human_Appearance_Transfer_CVPR_2018_paper.pdf)\n- Pose guided person image generation, NeurIPS 2017 - [Paper](https://arxiv.org/pdf/1705.09368.pdf), [Code](https://github.com/charliememory/Pose-Guided-Person-Image-Generation)\n\n\n\n\n## Datasets for Virtual Try-on\n- MV-Fashion, CVPR 2026 - [Project](https://hunorlaczko.github.io/MV-Fashion/)\n- Garments2Look, CVPR 2026 - [Project](https://artmesciencelab.github.io/Garments2Look/)\n- StreetTryOn - [Download](https://github.com/cuiaiyu/street-tryon-benchmark)\n- CLOTH4D, CVPR 2023 - [Download](https://github.com/AemikaChow/CLOTH4D)\n- DressCode - [Download](https://docs.google.com/forms/d/e/1FAIpQLSeWVzxWcj3JSALtthuw-2QDAbf2ymiK37sA4pRQD4tZz2vqsw/viewform), [Paper](https://arxiv.org/pdf/2204.08532.pdf)\n- VITON-HD - [Download](https://www.dropbox.com/s/10bfat0kg4si1bu/zalando-hd-resized.zip?dl=0), [Project](https://psh01087.github.io/VITON-HD/)\n- VITON - [Download](https://drive.google.com/file/d/1MxCUvKxejnwWnoZ-KoCyMCXo3TLhRuTo/view), [Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Han_VITON_An_Image-Based_CVPR_2018_paper.pdf)\n- MPV - [Download](https://drive.google.com/drive/folders/1e3ThRpSj8j9PaCUw8IrqzKPDVJK_grcA), [Paper](https://arxiv.org/abs/1902.11026)\n- Deep Fashion3D - [Paper](https://arxiv.org/abs/2003.12753)\n- DeepFashion MultiModal - [Download](https://github.com/yumingj/DeepFashion-MultiModal)\n- Digital Wardrobe - [Download/Paper/Project](http://virtualhumans.mpi-inf.mpg.de/mgn/)\n- TailorNet Dataset - [Download](https://github.com/zycliao/TailorNet_dataset), [Project](http://virtualhumans.mpi-inf.mpg.de/tailornet/)\n- CLOTH3D - [Paper](https://arxiv.org/abs/1912.02792)\n- 3DPeople - [Project](https://www.albertpumarola.com/research/3DPeople/index.html)\n- THUman Dataset - [Project](http://www.liuyebin.com/deephuman/deephuman.html)\n- Garment Dataset, Wang et al. 2018 - [Project](http://geometry.cs.ucl.ac.uk/projects/2018/garment_design/)\n\n\n\n\n## Safety and Moderation for Virtual Try-on\n\n- Try-On Safety Classifier: predicts what a try-on of a product would show (ok, revealing, lingerie, adult) - [Model](https://huggingface.co/Genlook/tryon-safety-classifier), [Blog](https://genlook.app/blog/open-source-tryon-safety-classifier)\n\n\n## Related Conference Workshops\n\n- Workshop on Virtual Try-On: [CVPR 2024](https://vto-cvpr24.github.io/)\n- Workshop on Computer Vision for Fashion, Art and Design: [CVPR 2024](https://sites.google.com/view/cvfad2024/home), [CVPR 2023](https://sites.google.com/view/cvfad2023/home), [CVPR 2022](https://sites.google.com/view/cvfad2022/home), [CVPR 2021](https://sites.google.com/zalando.de/cvfad2021/home), [CVPR 2020](https://sites.google.com/view/cvcreative2020), [ICCV 2019](https://sites.google.com/view/cvcreative), [ECCV 2018](https://sites.google.com/view/eccvfashion)\n- Workshop on Towards Human-Centric Image/Video Synthesis: [CVPR 2020](https://vuhcs.github.io/), [CVPR 2019](https://vuhcs.github.io/vuhcs-2019/index.html)\n\n\n\n\n## Demos\n\n- Outfit Anyone-in-the-Wild - [Demo](https://huggingface.co/spaces/selfit-camera/OutfitAnyone-in-the-Wild), [Docs](https://github.com/selfitcamera/Outfit-Anyone-in-the-Wild)\n- Outfit Anyone - [Demo](https://huggingface.co/spaces/HumanAIGC/OutfitAnyone), [Docs](https://github.com/HumanAIGC/OutfitAnyone)\n- Looklet Dressing Room [Business](https://looklet.com), [Demo](https://dressing-room.looklet.com)\n- TINT platform for virtual try-on of everything face-related (makeup, glasses, earrings, jewelry, etc.) [Business](https://www.banuba.com/solutions/e-commerce/virtual-try-on), [Demo](https://banuba.com/solutions/e-commerce/virtual-makeup-demo/).\n- Real-time virtual try-on - [Demo](https://github.com/ZaiqiangWu/RTV)\n- Virtual Try On AI - [Business/Demo](https://aitryon.art/)\n- Genlook virtual try-on for Ecommerce (garments, shoes, glasses) and Try-On API for developers. [Business](https://www.genlook.app/), [API](https://genlook.app/developers), [Demo](https://demo.genlook.app/products/t-shirt-manches-courtes-imprime)\n- Adstronaut AI - on-model AI photoshoots, garment editor, color and fabric swap, and tech packs for fashion brands. [Business](https://adstronaut.net)\n- STIL.AI - AI-powered fashion design generator. Describe your dream garment and get a unique AI-generated design in seconds. Free preview with watermark, high-res purchase from 9 SEK. [Demo](https://stil.gracestack.se/text-till-mode.html), [Business](https://stil.gracestack.se)\n- Kolors Virtual - AI fashion virtual try-on that generates images and videos of models wearing your outfits. [Business/Demo](https://www.kolorsvirtual.com/)\n- InkCraft - tattoo virtual try-on: warps the design to the body surface in the user's own photo rather than compositing it flat; the mobile app adds a 3D body model built from that photo for checking placement from other angles. Browser demo, no signup. [Business](https://inkcraftapp.com), [Demo](https://inkcraftapp.com/try)\n- Private Try-On - AI outfit style previews built from separate tops, bottoms, shoes, and optional accessories. [Business/Demo](https://privatetryon.com/)\n\n\n\n\n## Related Repositories\n\n- [awesome-fashion-ai](https://github.com/ayushidalmia/awesome-fashion-ai)\n- [Cool Fashion Papers](https://github.com/lzhbrian/Cool-Fashion-Papers)\n- [Clothes-3D](https://github.com/lzhbrian/Clothes-3D)\n- [Awesome 3D Human](https://github.com/lijiaman/awesome-3d-human)\n- [Awesome 3D reconstruction list](https://github.com/openMVG/awesome_3DReconstruction_list)\n- [Human Body Reconstruction](https://github.com/chenweikai/Body_Reconstruction_References)\n- [Awesome 3D Body Papers](https://github.com/3DFaceBody/awesome-3dbody-papers)\n- [Awesome pose transfer](https://github.com/Zhangjinso/Awesome-pose-transfer)\n\n\n#### Pull requests are welcome!\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/minar09%2Fawesome-virtual-try-on/projects"}