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Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models
March 27, 2024, 4:43 a.m. | Songwei Ge, Seungjun Nah, Guilin Liu, Tyler Poon, Andrew Tao, Bryan Catanzaro, David Jacobs, Jia-Bin Huang, Ming-Yu Liu, Yogesh Balaji
cs.LG updates on arXiv.org arxiv.org
Abstract: Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy. While off-the-shelf billion-scale datasets for image generation are available, collecting similar video data of the same scale is still challenging. Also, training a video diffusion model is computationally much more expensive than its image counterpart. In this work, we explore finetuning a pretrained image diffusion model with …
abstract animated arxiv billion correlation cs.cv cs.gr cs.lg data datasets diffusion diffusion models image image generation images noise photorealistic prior progress quality scale type video video data video diffusion
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