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arXiv · 2412.04929

Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction

Gaurav Shrivastava·Abhinav Shrivastava
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Abstract

Diffusion models have made significant strides in image generation, mastering tasks such as unconditional image synthesis, text-image translation, and image-to-image conversions. However, their capability falls short in the realm of video prediction, mainly because they treat videos as a collection of independent images, relying on external constraints such as temporal attention mechanisms to enforce temporal coherence. In our paper, we introduce a novel model class, that treats video as a continuous multi-dimensional process rather than a series of discrete frames. We also report a reduction of 75\% sampling steps required to sample a new frame thus making our framework more efficient during the inference time. Through extensive experimentation, we establish state-of-the-art performance in video prediction, validated on benchmark datasets including KTH, BAIR, Human3.6M, and UCF101. Navigate to the project page https://www.cs.umd.edu/~gauravsh/cvp/supp/website.html for video results.

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BibTeXRIS

Gaurav Shrivastava, Abhinav Shrivastava. 2024-12-06. Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction. https://arxiv.org/abs/2412.04929

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