Our proposed method for video generation utilizes the capabilities of large language models to create diverse videos from various input signals, showcasing enhanced performance.
In our experiments, we demonstrate the language model's ability to adapt to distinct tasks by employing modality-specific tokenization, allowing for effective video generation and related applications.
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