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arXiv · 2407.11814

Contrastive Sequential-Diffusion Learning: Non-linear and Multi-Scene Instructional Video Synthesis

Vasco Ramos·Yonatan Bitton·Michal Yarom·Idan Szpektor·Joao Magalhaes
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Abstract

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.

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Vasco Ramos, Yonatan Bitton, Michal Yarom, Idan Szpektor, Joao Magalhaes. 2024-07-16. Contrastive Sequential-Diffusion Learning: Non-linear and Multi-Scene Instructional Video Synthesis. https://arxiv.org/abs/2407.11814

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