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

TL;DR

This paper introduces a novel continuous multi-dimensional process model for video prediction, significantly improving efficiency and achieving state-of-the-art results on multiple benchmark datasets.

Contribution

The paper proposes modeling videos as continuous processes, reducing sampling steps by 75%, and establishing new state-of-the-art performance in video prediction.

Findings

75% reduction in sampling steps for inference

State-of-the-art performance on KTH, BAIR, Human3.6M, UCF101

Effective modeling of videos as continuous multi-dimensional processes

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.