  Action-centric sequence descriptions like recipe instructions and
do-it-yourself projects include non-linear patterns in which the next step may
require to be visually consistent not on the immediate previous step but on
earlier steps. Current video synthesis approaches fail to generate consistent
multi-scene videos for such task descriptions. 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 consistent visualisation. Our
experiments with real-world data demonstrate the practicality and improved
consistency of our model compared to prior work.
