MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909534583259136 |
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| author | Wan, Zhenchen xu, Yanwu Hu, Dongting Cheng, Weilun Chen, Tianxi Wang, Zhaoqing Liu, Feng Liu, Tongliang Gong, Mingming |
| author_facet | Wan, Zhenchen xu, Yanwu Hu, Dongting Cheng, Weilun Chen, Tianxi Wang, Zhaoqing Liu, Feng Liu, Tongliang Gong, Mingming |
| contents | Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garment detail preservation, driven by powerful text-to-image (T2I) diffusion models. However, existing methods often rely on user-provided masks, introducing complexity and performance degradation due to imperfect inputs, as shown in Fig.1(a). To address this, we propose a Mask-Free VITON (MF-VITON) framework that achieves realistic VITON using only a single person image and a target garment, eliminating the requirement for auxiliary masks. Our approach introduces a novel two-stage pipeline: (1) We leverage existing Mask-based VITON models to synthesize a high-quality dataset. This dataset contains diverse, realistic pairs of person images and corresponding garments, augmented with varied backgrounds to mimic real-world scenarios. (2) The pre-trained Mask-based model is fine-tuned on the generated dataset, enabling garment transfer without mask dependencies. This stage simplifies the input requirements while preserving garment texture and shape fidelity. Our framework achieves state-of-the-art (SOTA) performance regarding garment transfer accuracy and visual realism. Notably, the proposed Mask-Free model significantly outperforms existing Mask-based approaches, setting a new benchmark and demonstrating a substantial lead over previous approaches. For more details, visit our project page: https://zhenchenwan.github.io/MF-VITON/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input Wan, Zhenchen xu, Yanwu Hu, Dongting Cheng, Weilun Chen, Tianxi Wang, Zhaoqing Liu, Feng Liu, Tongliang Gong, Mingming Computer Vision and Pattern Recognition Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garment detail preservation, driven by powerful text-to-image (T2I) diffusion models. However, existing methods often rely on user-provided masks, introducing complexity and performance degradation due to imperfect inputs, as shown in Fig.1(a). To address this, we propose a Mask-Free VITON (MF-VITON) framework that achieves realistic VITON using only a single person image and a target garment, eliminating the requirement for auxiliary masks. Our approach introduces a novel two-stage pipeline: (1) We leverage existing Mask-based VITON models to synthesize a high-quality dataset. This dataset contains diverse, realistic pairs of person images and corresponding garments, augmented with varied backgrounds to mimic real-world scenarios. (2) The pre-trained Mask-based model is fine-tuned on the generated dataset, enabling garment transfer without mask dependencies. This stage simplifies the input requirements while preserving garment texture and shape fidelity. Our framework achieves state-of-the-art (SOTA) performance regarding garment transfer accuracy and visual realism. Notably, the proposed Mask-Free model significantly outperforms existing Mask-based approaches, setting a new benchmark and demonstrating a substantial lead over previous approaches. For more details, visit our project page: https://zhenchenwan.github.io/MF-VITON/. |
| title | MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.08650 |