  Video try-on is a challenging task and has not been well tackled in previous
works. The main obstacle lies in preserving the details of the clothing 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 first leverage the Kalman filter to construct smooth crops
in the focus tunnel and inject the position embedding of the tunnel 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 as supplementary cues. Equipped with these techniques,
Tunnel Try-on keeps the fine details of the clothing 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.
