MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input

• URL: http://arxiv.org/abs/2503.08650v1
• Date: Tue, 11 Mar 2025 17:40:59 GMT
• Title: MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input
• Authors: Zhenchen Wan, Yanwu xu, Dongting Hu, Weilun Cheng, Tianxi Chen, Zhaoqing Wang, Feng Liu, Tongliang Liu, Mingming Gong,
• Abstract summary: We propose a Mask-Free VITON framework that achieves realistic VITON using only a single person image and a target garment.<n>We leverage existing Mask-based VITON models to synthesize a high-quality dataset.<n>This dataset contains diverse, realistic pairs of person images and corresponding garments, augmented with varied backgrounds to mimic real-world scenarios.
• Score: 69.33864837012202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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