Abstract
Generating Long Videos of Dynamic Scenes
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 biases 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.
To this end, 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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Subjects: |
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) |
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Cite as: |
arXiv:2206.03429 [cs.CV] |
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(or arXiv:2206.03429v2 [cs.CV] for this version) |
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Code, datasets, pretrained models