2 min read

New AI: 6,000,000,000 Steps In 24 Hours!

New AI: 6,000,000,000 Steps In 24 Hours!
๐Ÿ†• from Two Minute Papers! Learn how an AI algorithm can control virtual characters with different morphologies using a three-point input. The training process is efficient and requires minimal data. #AI #VirtualCharacters.

Key Takeaways at a Glance

  1. 00:00 Virtual characters can be controlled with different morphologies.
  2. 01:07 Reinforcement learning can improve the control of virtual characters.
  3. 02:27 Additional steps can enhance the realism of virtual character control.
  4. 02:58 The algorithm can perform movements across different morphologies.
  5. 04:36 The training process is efficient and requires minimal data.
  6. 05:31 The paper highlights the importance of sharing and discussing AI research.
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1. Virtual characters can be controlled with different morphologies.

๐Ÿฅˆ85 00:00

Using a three-point input, virtual characters can be controlled with different morphologies, such as a tiny mouse or a dinosaur.

  • This is achieved without using full-body motion capture.
  • The algorithm only needs to track the movement of the VR headset and two controllers.

2. Reinforcement learning can improve the control of virtual characters.

๐Ÿฅ‡92 01:07

By using reinforcement learning, an AI agent can learn to control virtual characters more effectively.

  • The agent can learn from motion capture data and improve the jerky animation.
  • After training, the motion capture data is no longer needed, and only the headset and two controllers are required.

3. Additional steps can enhance the realism of virtual character control.

๐Ÿฅˆ88 02:27

The paper suggests enriching the training data with additional information to improve the AI's learning.

  • This additional information can be discarded after training.
  • The algorithm can then produce more realistic movements, even controlling the tail of a dinosaur.

4. The algorithm can perform movements across different morphologies.

๐Ÿฅ‡91 02:58

The algorithm can perform movements across different morphologies and character types in an energy-efficient manner.

  • It can generate smooth and elegant movements, even for larger or smaller characters.
  • The algorithm only requires information from the headset and controllers, not the lower body.

5. The training process is efficient and requires minimal data.

๐Ÿฅˆ87 04:36

The training process only requires 24 hours and a consumer graphics card.

  • The algorithm can generalize movements from just 4 hours of training data.
  • The potential for further automation and improvement is promising.

6. The paper highlights the importance of sharing and discussing AI research.

๐Ÿฅˆ83 05:31

The paper emphasizes the need to spread awareness and appreciation for AI research in computer graphics.

  • The author encourages fellow scholars to help promote these amazing papers.
  • The work showcased in the paper deserves more recognition and discussion.
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