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

FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery