MAGVIT: Masked Generative Video Transformer

• URL: http://arxiv.org/abs/2212.05199v2
• Date: Wed, 5 Apr 2023 02:32:59 GMT
• Title: MAGVIT: Masked Generative Video Transformer
• Authors: Lijun Yu, Yong Cheng, Kihyuk Sohn, Jos\'e Lezama, Han Zhang, Huiwen Chang, Alexander G. Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, Lu Jiang
• Abstract summary: We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains.
• Score: 129.50814875955444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at https://magvit.cs.cmu.edu.

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