Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VideoGPT: Video Generation using VQ-VAE and Transformers   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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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph