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.
@article{arxiv.2508.21070,
title = {Dress&Dance: Dress up and Dance as You Like It - Technical Preview},
author = {Jun-Kun Chen and Aayush Bansal and Minh Phuoc Vo and Yu-Xiong Wang},
journal= {arXiv preprint arXiv:2508.21070},
year = {2025}
}