Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos
Zhengze Xu, Mengyue Chen, Zhao Wang, Linyu Xing, Zhonghua Zhai, Nong Sang, Jinsong Lan, Shuai Xiao, Changxin Gao
Abstract
Video try-on is challenging and has not been well tackled in previous works. The main obstacle lies in preserving the clothing details and modeling the coherent motions simultaneously. Faced with those difficulties, we address video try-on by proposing a diffusion-based framework named ''Tunnel Try-on.'' The core idea is excavating a ''focus tunnel'' in the input video that gives close-up shots around the clothing regions. We zoom in on the region in the tunnel to better preserve the fine details of the clothing. To generate coherent motions, we leverage the Kalman filter to smooth the tunnel and inject its position embedding into attention layers to improve the continuity of the generated videos. In addition, we develop an environment encoder to extract the context information outside the tunnels. Equipped with these techniques, Tunnel Try-on keeps fine clothing details and synthesizes stable and smooth videos. Demonstrating significant advancements, Tunnel Try-on could be regarded as the first attempt toward the commercial-level application of virtual try-on in videos. The project page is https://mengtingchen.github.io/tunnel-try-on-page/.