# IMAGDressing-v1: Customizable Virtual Dressing

arXiv: https://arxiv.org/abs/2407.12705 · HTML: https://mlsanity.com/paper/2407.12705/

## Key facts

- Cited by 100 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 35 of its 44 references that are in the map.
- First posted on arXiv 17 Jul 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 35)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model](https://mlsanity.com/paper/2401.16420/) (2024)
- [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)
- [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://mlsanity.com/paper/2211.08332/) (2022)
- [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)
- [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)
- [Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow](https://mlsanity.com/paper/2308.06101/) (2023)
- [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)
- [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color](https://mlsanity.com/paper/1902.06838/) (2019)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On](https://mlsanity.com/paper/2007.02721/) (2020)
- [Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models](https://mlsanity.com/paper/2407.02482/) (2024)

## Built on by (most cited first, 30 of 100)

- [DreamO: A Unified Framework for Image Customization](https://mlsanity.com/paper/2504.16915/) (2025)
- [CSGO: Content-Style Composition in Text-to-Image Generation](https://mlsanity.com/paper/2408.16766/) (2024)
- [Parameter-Efficient Fine-Tuning for Foundation Models](https://mlsanity.com/paper/2501.13787/) (2025)
- [IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design](https://mlsanity.com/paper/2504.13176/) (2025)
- [UniREditBench: A Unified Reasoning-based Image Editing Benchmark](https://mlsanity.com/paper/2511.01295/) (2025)
- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [FaceShot: Bring Any Character into Life](https://mlsanity.com/paper/2503.00740/) (2025)
- [Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering](https://mlsanity.com/paper/2505.12831/) (2025)
- [Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization](https://mlsanity.com/paper/2506.15980/) (2025)
- [PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards](https://mlsanity.com/paper/2512.01236/) (2025)
- [GLDiTalker: Speech-Driven 3D Facial Animation with Graph Latent Diffusion Transformer](https://mlsanity.com/paper/2408.01826/) (2024)
- [Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off](https://mlsanity.com/paper/2508.04825/) (2025)
- [Steering When Necessary: Flexible Steering Large Language Models with Backtracking](https://mlsanity.com/paper/2508.17621/) (2025)
- [Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model](https://mlsanity.com/paper/2409.19608/) (2024)
- [Controllable Human Image Generation with Personalized Multi-Garments](https://mlsanity.com/paper/2411.16801/) (2024)
- [Re-Attentional Controllable Video Diffusion Editing](https://mlsanity.com/paper/2412.11710/) (2024)
- [StyleTailor: Towards Personalized Fashion Styling via Hierarchical Negative Feedback](https://mlsanity.com/paper/2508.06555/) (2025)
- [EmojiDiff: Advanced Facial Expression Control with High Identity Preservation in Portrait Generation](https://mlsanity.com/paper/2412.01254/) (2024)
- [ITVTON: Virtual Try-On Diffusion Transformer Based on Integrated Image and Text](https://mlsanity.com/paper/2501.16757/) (2025)
- [Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling](https://mlsanity.com/paper/2412.20725/) (2024)
- [FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation](https://mlsanity.com/paper/2505.17544/) (2025)
- [EHCTNet: Enhanced Hybrid of CNN and Transformer Network for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2501.01238/) (2025)
- [Training-Free, Identity-Preserving Image Editing for Fashion Pose Alignment and Normalization](https://mlsanity.com/paper/2501.13692/) (2025)
- [Efficient Feature Fusion for UAV Object Detection](https://mlsanity.com/paper/2501.17983/) (2025)
- [MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks](https://mlsanity.com/paper/2509.14638/) (2025)
- [Teleportraits: Training-Free People Insertion into Any Scene](https://mlsanity.com/paper/2510.05660/) (2025)
- [OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation](https://mlsanity.com/paper/2601.22725/) (2026)
- [M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production](https://mlsanity.com/paper/2603.23617/) (2026)
- [HiFiSeg: High-Frequency Information Enhanced Polyp Segmentation with Global-Local Vision Transformer](https://mlsanity.com/paper/2410.02528/) (2024)
- [DH-VTON: Deep Text-Driven Virtual Try-On via Hybrid Attention Learning](https://mlsanity.com/paper/2410.12501/) (2024)

## Abstract (arXiv)

Latest advances have achieved realistic virtual try-on (VTON) through localized garment inpainting using latent diffusion models, significantly enhancing consumers' online shopping experience. However, existing VTON technologies neglect the need for merchants to showcase garments comprehensively, including flexible control over garments, optional faces, poses, and scenes. To address this issue, we define a virtual dressing (VD) task focused on generating freely editable human images with fixed garments and optional conditions. Meanwhile, we design a comprehensive affinity metric index (CAMI) to evaluate the consistency between generated images and reference garments. Then, we propose IMAGDressing-v1, which incorporates a garment UNet that captures semantic features from CLIP and texture features from VAE. We present a hybrid attention module, including a frozen self-attention and a trainable cross-attention, to integrate garment features from the garment UNet into a frozen denoising UNet, ensuring users can control different scenes through text. IMAGDressing-v1 can be combined with other extension plugins, such as ControlNet and IP-Adapter, to enhance the diversity and controllability of generated images. Furthermore, to address the lack of data, we release the interactive garment pairing (IGPair) dataset, containing over 300,000 pairs of clothing and dressed images, and establish a standard pipeline for data assembly. Extensive experiments demonstrate that our IMAGDressing-v1 achieves state-of-the-art human image synthesis performance under various controlled conditions. The code and model will be available at https://github.com/muzishen/IMAGDressing.