Dress&Dance: Dress up and Dance as You Like It - Technical Preview

• URL: http://arxiv.org/abs/2508.21070v1
• Date: Thu, 28 Aug 2025 17:59:55 GMT
• Title: Dress&Dance: Dress up and Dance as You Like It - Technical Preview
• Authors: Jun-Kun Chen, Aayush Bansal, Minh Phuoc Vo, Yu-Xiong Wang,
• Abstract summary: Dress&Dance is a video diffusion framework that generates high quality 5-second-long 24 FPS virtual try-on videos.<n>Our approach requires a single user image and supports a range of tops, bottoms, and one-piece garments, as well as simultaneous tops and bottoms try-on in a single pass.
• Score: 55.78895889755938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Dress&Dance, a video diffusion framework that generates high quality 5-second-long 24 FPS virtual try-on videos at 1152x720 resolution of a user wearing desired garments while moving in accordance with a given reference video. Our approach requires a single user image and supports a range of tops, bottoms, and one-piece garments, as well as simultaneous tops and bottoms try-on in a single pass. Key to our framework is CondNet, a novel conditioning network that leverages attention to unify multi-modal inputs (text, images, and videos), thereby enhancing garment registration and motion fidelity. CondNet is trained on heterogeneous training data, combining limited video data and a larger, more readily available image dataset, in a multistage progressive manner. Dress&Dance outperforms existing open source and commercial solutions and enables a high quality and flexible try-on experience.

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