  Our study introduces a new image-to-video generator called FashionFlow to
generate fashion videos. By utilising a diffusion model, we are able to create
short videos from still fashion images. Our approach involves developing and
connecting relevant components with the diffusion model, which results in the
creation of high-fidelity videos that are aligned with the conditional image.
The components include the use of pseudo-3D convolutional layers to generate
videos efficiently. VAE and CLIP encoders capture vital characteristics from
still images to condition the diffusion model at a global level. Our research
demonstrates a successful synthesis of fashion videos featuring models posing
from various angles, showcasing the fit and appearance of the garment. Our
findings hold great promise for improving and enhancing the shopping experience
for the online fashion industry.
