Phenaki from Google Brain explained | Generating long form video with Transformers

Phenaki from Google Brain explained | Generating long form video with Transformers

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How to create story-like videos with transformers – and no diffusion models are involved! In this video, we explain the Phenaki paper from Google Brain. 🧠
β–Ί Sponsor: Tasq.ai πŸ‘‰ https://www.tasq.ai/

πŸ“Ί Imagen Video and Make-A-Video explained: https://youtu.be/AcvmyqGgMh8
πŸ“Ί Stable Diffusion explained: https://youtu.be/J87hffSMB60
πŸ“Ί Diffusion models explained: https://youtu.be/344w5h24-h8

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πŸ“œ Villegas, Ruben, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. "Phenaki: Variable Length Video Generation From Open Domain Textual Description." arXiv preprint arXiv:2210.02399 (2022). https://arxiv.org/abs/2210.02399

πŸ”— Phenaki website: https://phenaki.github.io/

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Dres. Trost GbR, Edvard GrΓΈdem, Vignesh Valliappan, Mutual Information, Mike Ton

Outline:
00:00 Phenaki
00:56 Tasq.ai [Sponsor]
02:18 The problem of long video training data
04:02 Phenaki in a nutshell
06:22 First component: The C-ViViT architecture explained
09:25 Second component: MaskGIT in Phenaki

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