VideoPoet: A Large Language Model for Zero-Shot Video Generation
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Cited by 112 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting VideoPoet's ability to generate high-fidelity motions. Project page: http://sites.research.google/videopoet/
Key facts. Cited by 479 papers in the map (data through 7 Oct 2026). Builds on 82 of its 97 references that are in the map. First posted on arXiv 21 Dec 2023.
Builds on
82 of 97 references.
References · 82
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