Generated video scenes for action-centric sequence descriptions, such as recipe instructions and do-it-yourself projects, often include non-linear patterns, where the next video may need to be visually consistent not with the immediately preceding video but with earlier ones. Current multi-scene video synthesis approaches fail to meet these consistency requirements. To address this, we propose a contrastive sequential video diffusion method that selects the most suitable previously generated scene to guide and condition the denoising process of the next scene. The result is a multi-scene video that is grounded in the scene descriptions and coherent w.r.t. the scenes that require visual consistency. Experiments with action-centered data from the real world demonstrate the practicality and improved consistency of our model compared to previous work.
@article{arxiv.2407.11814,
title = {Contrastive Sequential-Diffusion Learning: Non-linear and Multi-Scene Instructional Video Synthesis},
author = {Vasco Ramos and Yonatan Bitton and Michal Yarom and Idan Szpektor and Joao Magalhaes},
journal= {arXiv preprint arXiv:2407.11814},
year = {2024}
}