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Bye James Cameron! Meta AI's NEW Movie Gen Model

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Manage episode 444202211 series 3605861
Content provided by Brian Carter. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Brian Carter or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This research paper from Meta AI describes "Movie Gen," a series of foundational models capable of generating high-quality videos and synchronized audio. The paper discusses the models' capabilities, including text-to-video synthesis, video personalization, video editing, and audio generation. It outlines the architecture, training process, and evaluation metrics used for each model, along with a detailed comparison to existing techniques and commercial systems. The authors emphasize the importance of scaling training data, compute, and model parameters for improved performance and explore different parallelism techniques to optimize the training process. The paper also delves into the challenges of evaluating video generation and audio generation, highlighting the need for human evaluation alongside automated metrics to assess complex tasks such as motion naturalness and text alignment.

Check out more here: https://ai.meta.com/research/movie-gen/

  continue reading

71 episodes

Artwork
iconShare
 
Manage episode 444202211 series 3605861
Content provided by Brian Carter. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Brian Carter or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This research paper from Meta AI describes "Movie Gen," a series of foundational models capable of generating high-quality videos and synchronized audio. The paper discusses the models' capabilities, including text-to-video synthesis, video personalization, video editing, and audio generation. It outlines the architecture, training process, and evaluation metrics used for each model, along with a detailed comparison to existing techniques and commercial systems. The authors emphasize the importance of scaling training data, compute, and model parameters for improved performance and explore different parallelism techniques to optimize the training process. The paper also delves into the challenges of evaluating video generation and audio generation, highlighting the need for human evaluation alongside automated metrics to assess complex tasks such as motion naturalness and text alignment.

Check out more here: https://ai.meta.com/research/movie-gen/

  continue reading

71 episodes

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