Poster

Generating Long Videos of Dynamic Scenes

Tim Brooks · Janne Hellsten · Miika Aittala · Ting-Chun Wang · Timo Aila · Jaakko Lehtinen · Ming-Yu Liu · Alexei Efros · Tero Karras

Hall J #342

Keywords: [ video generation ] [ Dynamics ] [ long videos ] [ generative model ] [ GAN ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Wed 30 Nov 9 a.m. PST — 11 a.m. PST

Abstract:

We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time while maintaining consistencies expected in real environments, such as plausible dynamics and object persistence. A common failure case is for content to never change due to over-reliance on inductive bias to provide temporal consistency, such as a single latent code that dictates content for the entire video. On the other extreme, without long-term consistency, generated videos may morph unrealistically between different scenes. To address these limitations, we prioritize the time axis by redesigning the temporal latent representation and learning long-term consistency from data by training on longer videos. We leverage a two-phase training strategy, where we separately train using longer videos at a low resolution and shorter videos at a high resolution. To evaluate the capabilities of our model, we introduce two new benchmark datasets with explicit focus on long-term temporal dynamics.

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