# MGT: Extending Virtual Try-Off to Multi-Garment Scenarios

arXiv: https://arxiv.org/abs/2504.13078 · HTML: https://mlsanity.com/paper/2504.13078/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 48 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 30 of 35)

- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [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)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://mlsanity.com/paper/2403.01779/) (2024)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)
- [CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2407.15886/) (2024)
- [Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2304.02051/) (2023)
- [Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16223/) (2023)
- [Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN](https://mlsanity.com/paper/2111.10544/) (2021)
- [The Conditional Analogy GAN: Swapping Fashion Articles on People Images](https://mlsanity.com/paper/1709.04695/) (2017)
- [TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models](https://mlsanity.com/paper/2411.18350/) (2024)
- [ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design](https://mlsanity.com/paper/2208.05621/) (2022)

## Built on by (most cited first, 6 of 6)

- [Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals](https://mlsanity.com/paper/2505.21062/) (2025)
- [Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories](https://mlsanity.com/paper/2603.14153/) (2026)
- [OmniDiT: Extending Diffusion Transformer to Omni-VTON Framework](https://mlsanity.com/paper/2603.19643/) (2026)
- [BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off](https://mlsanity.com/paper/2603.09236/) (2026)
- [Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off](https://mlsanity.com/paper/2603.22607/) (2026)
- [TrendGen: An Outfit Recommendation and Display System](https://mlsanity.com/paper/2603.27264/) (2026)

## Abstract (arXiv)

Computer vision is transforming fashion industry through Virtual Try-On (VTON) and Virtual Try-Off (VTOFF). VTON generates images of a person in a specified garment using a target photo and a standardized garment image, while a more challenging variant, Person-to-Person Virtual Try-On (p2p-VTON), uses a photo of another person wearing the garment. VTOFF, in contrast, extracts standardized garment images from photos of clothed individuals. We introduce Multi-Garment TryOffDiff (MGT), a diffusion-based VTOFF model capable of handling diverse garment types, including upper-body, lower-body, and dresses. MGT builds on a latent diffusion architecture with SigLIP-based image conditioning to capture garment characteristics such as shape, texture, and pattern. To address garment diversity, MGT incorporates class-specific embeddings, achieving state-of-the-art VTOFF results on VITON-HD and competitive performance on DressCode. When paired with VTON models, it further enhances p2p-VTON by reducing unwanted attribute transfer, such as skin tone, ensuring preservation of person-specific characteristics. Demo, code, and models are available at: https://rizavelioglu.github.io/tryoffdiff/