BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data

• URL: http://arxiv.org/abs/2609.04120v1
• Date: Thu, 03 Sep 2026 17:22:33 GMT
• Title: BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data
• Abstract summary: Video virtual try on (VVT) aims to generate realistic videos of a person wearing a target garment.<n>Recent methods leverage a-driven video generation paradigm to improve in-the-wild performance, yet they rely on masks to localize try-on regions.<n>We propose Boo-M-VVT, a mask-free VVT framework built upon the paradigm.
• Score: 16.374416879388352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video virtual try-on (VVT) aims to generate realistic videos of a person wearing a target garment. Recent methods leverage a keyframe-driven video generation paradigm to improve in-the-wild performance, yet they still rely on masks to localize try-on regions, making them vulnerable to large motions and severe occlusions. Although mask-free image-based try-on methods have shown promising results by leveraging large-scale pseudo data, extending this paradigm to videos remains difficult, as constructing video-level pseudo data is prohibitively expensive. Furthermore, coarse keyframe sampling and the scarcity of multi-view try-on data limit existing keyframe-driven methods in maintaining garment consistency and handling diverse try-on tasks. To address these challenges, we propose BooM-VVT, a mask-free VVT framework built upon the keyframe-driven paradigm. To achieve mask-free VVT, we introduce a multi-stage training strategy that leverages image-level pseudo data for mask-free localization learning, substantially reducing the need for costly video-level pseudo data. To improve garment consistency, we propose Garment-Sensitive Keyframe Sampling, which selects keyframes based on garment-relevant body regions to better capture garment appearance. We further introduce Frame-Shared 3D-RoPE to establish spatiotemporal correspondences between keyframes and target video frames for accurate garment-detail transfer. Finally, we construct OmniView, a large-scale multi-view try-on dataset to support reliable try-on video generation under complex camera viewpoints and diverse try-on tasks. Extensive experiments demonstrate that BooM-VVT achieves superior temporal consistency and garment fidelity over existing methods. Project page: https://boomvvt.github.io/boomvvt.

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