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🎬 One-Minute Video Generation via Test-Time Transformer Training

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Manage episode 477542580 series 3605659
Content provided by Kabir. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kabir 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://podcastplayer.com/legal.

Researchers introduced Test-Time Training (TTT) layers to enhance the ability of pre-trained Diffusion Transformers to generate longer, more complex videos from text. These novel layers, inspired by meta-learning, allow the model's hidden states to adapt during the video generation process. To validate their approach, they created a dataset of annotated Tom and Jerry cartoons for training and evaluation. Their model, incorporating TTT layers, outperformed existing methods in generating coherent, minute-long videos with multi-scene stories and dynamic motion, as judged by human evaluators. While promising, the generated videos still exhibit some artifacts, and the method's efficiency could be improved. The study demonstrates a step forward in creating longer, story-driven videos from textual descriptions.

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Podcast:
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251 episodes

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Manage episode 477542580 series 3605659
Content provided by Kabir. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kabir 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://podcastplayer.com/legal.

Researchers introduced Test-Time Training (TTT) layers to enhance the ability of pre-trained Diffusion Transformers to generate longer, more complex videos from text. These novel layers, inspired by meta-learning, allow the model's hidden states to adapt during the video generation process. To validate their approach, they created a dataset of annotated Tom and Jerry cartoons for training and evaluation. Their model, incorporating TTT layers, outperformed existing methods in generating coherent, minute-long videos with multi-scene stories and dynamic motion, as judged by human evaluators. While promising, the generated videos still exhibit some artifacts, and the method's efficiency could be improved. The study demonstrates a step forward in creating longer, story-driven videos from textual descriptions.

Send us a text

Support the show

Podcast:
https://kabir.buzzsprout.com
YouTube:
https://www.youtube.com/@kabirtechdives
Please subscribe and share.

  continue reading

251 episodes

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