Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
Jun-Kun Chen, Aayush Bansal, Minh Phuoc Vo, Yu-Xiong Wang
Abstract
This paper proposes Virtual Fitting Room (VFR), a novel video generative model that produces arbitrarily long virtual try-on videos. Our VFR models long video generation tasks as an auto-regressive, segment-by-segment generation process, eliminating the need for resource-intensive generation and lengthy video data, while providing the flexibility to generate videos of arbitrary length. The key challenges of this task are twofold: ensuring local smoothness between adjacent segments and maintaining global temporal consistency across different segments. To address these challenges, we propose our VFR framework, which ensures smoothness through a prefix video condition and enforces consistency with the anchor video — a 360°-view video that comprehensively captures the human's whole-body appearance. Our VFR generates minute-scale virtual try-on videos with both local smoothness and global temporal consistency under various motions, making it a pioneering work in long virtual try-on video generation. Project Page: https://immortalco.github.io/VirtualFittingRoom/.
BibTeX
@inproceedings{
chen2025virtual,
title={Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image},
author={Jun-Kun Chen and Aayush Bansal and Minh Phuoc Vo and Yu-Xiong Wang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=mbmGNCFc75}
}