# PromptDresser: Improving the Quality and Controllability of Virtual Try-On via Generative Textual Prompt and Prompt-aware Mask

arXiv: https://arxiv.org/abs/2412.16978 · HTML: https://mlsanity.com/paper/2412.16978/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- First posted on arXiv 22 Dec 2024.

## Placed in

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

## Built on by (most cited first, 17 of 17)

- [FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models](https://mlsanity.com/paper/2508.20586/) (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)
- [TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On](https://mlsanity.com/paper/2604.27958/) (2026)
- [FitControler: Toward Fit-Aware Virtual Try-On](https://mlsanity.com/paper/2512.24016/) (2025)
- [OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation](https://mlsanity.com/paper/2601.22725/) (2026)
- [RefTon: Reference person shot assist virtual Try-on](https://mlsanity.com/paper/2511.00956/) (2025)
- [CORAL: Correspondence Alignment for Improved Virtual Try-On](https://mlsanity.com/paper/2602.17636/) (2026)
- [Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On](https://mlsanity.com/paper/2607.21694/) (2026)
- [Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise](https://mlsanity.com/paper/2511.18775/) (2025)
- [PROMO: Promptable Outfitting for Efficient High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2603.11675/) (2026)
- [FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On](https://mlsanity.com/paper/2604.08526/) (2026)
- [MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On](https://mlsanity.com/paper/2606.11148/) (2026)
- [FitVTON: Fit-aware Virtual Try-On via Body-Garment Size Control](https://mlsanity.com/paper/2606.12012/) (2026)
- [CtrlVTON: Controllable Virtual Try-On via Visual-Instance-Prompt Segmentation](https://mlsanity.com/paper/2607.09362/) (2026)
- [TAMF-VTON: Texture-Aware Mask-Free Virtual Try-On via High-Fidelity Image Synthesis](https://mlsanity.com/paper/2607.14807/) (2026)
- [WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment](https://mlsanity.com/paper/2607.19923/) (2026)
- [TryOnReward: Learning Foveated Consistency for Reinforcement Fine-Tuning of Virtual Try-On](https://mlsanity.com/paper/2609.13259/) (2026)

## Abstract (arXiv)

Recent virtual try-on approaches have advanced by finetuning pre-trained text-to-image diffusion models to leverage their powerful generative ability. However, the use of text prompts in virtual try-on remains underexplored. This paper tackles a text-editable virtual try-on task that modifies the clothing based on the provided clothing image while editing the wearing style (e.g., tucking style, fit) according to the text descriptions. In the text-editable virtual try-on, three key aspects exist: (i) designing rich text descriptions for paired person-clothing data to train the model, (ii) addressing the conflicts where textual information of the existing person's clothing interferes the generation of the new clothing, and (iii) adaptively adjust the inpainting mask aligned with the text descriptions, ensuring proper editing areas while preserving the original person's appearance irrelevant to the new clothing. To address these aspects, we propose PromptDresser, a text-editable virtual try-on model that leverages large multimodal model (LMM) assistance to enable high-quality and versatile manipulation based on generative text prompts. Our approach utilizes LMMs via in-context learning to generate detailed text descriptions for person and clothing images independently, including pose details and editing attributes using minimal human cost. Moreover, to ensure the editing areas, we adjust the inpainting mask depending on the text prompts adaptively. Our approach enhances text editability while effectively conveying clothing details that are difficult to capture through images alone, leading to improved image quality. Experiments show that PromptDresser significantly outperforms baselines, demonstrating superior text-driven control and versatile clothing manipulation. Our code is available at https://github.com/rlawjdghek/PromptDresser.