  Video virtual try-on aims to transfer a clothing item onto the video of a
target person. Directly applying the technique of image-based try-on to the
video domain in a frame-wise manner will cause temporal-inconsistent outcomes
while previous video-based try-on solutions can only generate low visual
quality and blurring results. In this work, we present ViViD, a novel framework
employing powerful diffusion models to tackle the task of video virtual try-on.
Specifically, we design the Garment Encoder to extract fine-grained clothing
semantic features, guiding the model to capture garment details and inject them
into the target video through the proposed attention feature fusion mechanism.
To ensure spatial-temporal consistency, we introduce a lightweight Pose Encoder
to encode pose signals, enabling the model to learn the interactions between
clothing and human posture and insert hierarchical Temporal Modules into the
text-to-image stable diffusion model for more coherent and lifelike video
synthesis. Furthermore, we collect a new dataset, which is the largest, with
the most diverse types of garments and the highest resolution for the task of
video virtual try-on to date. Extensive experiments demonstrate that our
approach is able to yield satisfactory video try-on results. The dataset,
codes, and weights will be publicly available. Project page:
https://becauseimbatman0.github.io/ViViD.
