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arXiv · 2104.10157

VideoGPT: Video Generation using VQ-VAE and Transformers

Wilson Yan·Yunzhi Zhang·Pieter Abbeel·Aravind Srinivas
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Abstract

We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos. VideoGPT uses VQ-VAE that learns downsampled discrete latent representations of a raw video by employing 3D convolutions and axial self-attention. A simple GPT-like architecture is then used to autoregressively model the discrete latents using spatio-temporal position encodings. Despite the simplicity in formulation and ease of training, our architecture is able to generate samples competitive with state-of-the-art GAN models for video generation on the BAIR Robot dataset, and generate high fidelity natural videos from UCF-101 and Tumbler GIF Dataset (TGIF). We hope our proposed architecture serves as a reproducible reference for a minimalistic implementation of transformer based video generation models. Samples and code are available at https://wilson1yan.github.io/videogpt/index.html

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Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas. 2021-04-20. VideoGPT: Video Generation using VQ-VAE and Transformers. https://arxiv.org/abs/2104.10157

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