Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

• URL: http://arxiv.org/abs/2205.09853v1
• Date: Thu, 19 May 2022 20:58:05 GMT
• Title: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
• Authors: Vikram Voleti and Alexia Jolicoeur-Martineau and Christopher Pal
• Abstract summary: Masked Conditional Video Diffusion (MCVD) is a general-purpose framework for video prediction. We train the model in a manner where we randomly and independently mask all the past frames or all the future frames. Our approach yields SOTA results across standard video prediction benchmarks, with computation times measured in 1-12 days.
• Score: 14.631523634811392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult. Furthermore, existing prediction frameworks are typically not capable of simultaneously handling other video-related tasks such as unconditional generation or interpolation. In this work, we devise a general-purpose framework called Masked Conditional Video Diffusion (MCVD) for all of these video synthesis tasks using a probabilistic conditional score-based denoising diffusion model, conditioned on past and/or future frames. We train the model in a manner where we randomly and independently mask all the past frames or all the future frames. This novel but straightforward setup allows us to train a single model that is capable of executing a broad range of video tasks, specifically: future/past prediction -- when only future/past frames are masked; unconditional generation -- when both past and future frames are masked; and interpolation -- when neither past nor future frames are masked. Our experiments show that this approach can generate high-quality frames for diverse types of videos. Our MCVD models are built from simple non-recurrent 2D-convolutional architectures, conditioning on blocks of frames and generating blocks of frames. We generate videos of arbitrary lengths autoregressively in a block-wise manner. Our approach yields SOTA results across standard video prediction and interpolation benchmarks, with computation times for training models measured in 1-12 days using $\le$ 4 GPUs. https://mask-cond-video-diffusion.github.io

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