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

Autor:	Shrivastava, Gaurav, Shrivastava, Abhinav
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.} Comment: Navigate to the project page https://www.cs.umd.edu/~gauravsh/cvp/supp/website.html for video results. Extended version of published CVPR paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.04929 Zobrazit plný text záznamu View this record from Arxiv