# MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization

arXiv: https://arxiv.org/abs/2508.08488 · HTML: https://mlsanity.com/paper/2508.08488/

## Key facts

- Cited by 2 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 32 of its 45 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## 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 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [OminiControl: Minimal and Universal Control for Diffusion Transformer](https://mlsanity.com/paper/2411.15098/) (2024)
- [Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://mlsanity.com/paper/2405.08748/) (2024)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [Sapiens: Foundation for Human Vision Models](https://mlsanity.com/paper/2408.12569/) (2024)
- [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)
- [TryOnDiffusion: A Tale of Two UNets](https://mlsanity.com/paper/2306.08276/) (2023)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [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)
- [CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2407.15886/) (2024)
- [Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks](https://mlsanity.com/paper/2501.15891/) (2025)
- [HandRefiner: Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting](https://mlsanity.com/paper/2311.17957/) (2023)
- [Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2404.01089/) (2024)
- [Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images](https://mlsanity.com/paper/2311.16094/) (2023)
- [M&M VTO: Multi-Garment Virtual Try-On and Editing](https://mlsanity.com/paper/2406.04542/) (2024)
- [Forgedit: Text Guided Image Editing via Learning and Forgetting](https://mlsanity.com/paper/2309.10556/) (2023)
- [From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation](https://mlsanity.com/paper/2404.15267/) (2024)
- [OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup](https://mlsanity.com/paper/2301.00965/) (2023)

## Built on by (most cited first, 2 of 2)

- [CtrlVTON: Controllable Virtual Try-On via Visual-Instance-Prompt Segmentation](https://mlsanity.com/paper/2607.09362/) (2026)
- [Layering Virtual Try-On](https://mlsanity.com/paper/2607.22924/) (2026)

## Abstract (arXiv)

Virtual try-on seeks to generate photorealistic images of individuals in desired garments, a task that must simultaneously preserve personal identity and garment fidelity for practical use in fashion retail and personalization. However, existing methods typically handle upper and lower garments separately, rely on heavy preprocessing, and often fail to preserve person-specific cues such as tattoos, accessories, and body shape-resulting in limited realism and flexibility. To this end, we introduce MuGa-VTON, a unified multi-garment diffusion framework that jointly models upper and lower garments together with person identity in a shared latent space. Specifically, we proposed three key modules: the Garment Representation Module (GRM) for capturing both garment semantics, the Person Representation Module (PRM) for encoding identity and pose cues, and the A-DiT fusion module, which integrates garment, person, and text-prompt features through a diffusion transformer. This architecture supports prompt-based customization, allowing fine-grained garment modifications with minimal user input. Extensive experiments on the VITON-HD and DressCode benchmarks demonstrate that MuGa-VTON outperforms existing methods in both qualitative and quantitative evaluations, producing high-fidelity, identity-preserving results suitable for real-world virtual try-on applications.