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

1. Virtual characters can be controlled with different morphologies.

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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.

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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.

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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.

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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.

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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.

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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.