Nvidia corporation (20240135618). GENERATING ARTIFICIAL AGENTS FOR REALISTIC MOTION SIMULATION USING BROADCAST VIDEOS simplified abstract
Contents
- 1 GENERATING ARTIFICIAL AGENTS FOR REALISTIC MOTION SIMULATION USING BROADCAST VIDEOS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING ARTIFICIAL AGENTS FOR REALISTIC MOTION SIMULATION USING BROADCAST VIDEOS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
GENERATING ARTIFICIAL AGENTS FOR REALISTIC MOTION SIMULATION USING BROADCAST VIDEOS
Organization Name
Inventor(s)
Haotian Zhang of Stanford CA (US)
Ye Yuan of Santa Clara CA (US)
Viktor Makoviichuk of Santa Clara CA (US)
GENERATING ARTIFICIAL AGENTS FOR REALISTIC MOTION SIMULATION USING BROADCAST VIDEOS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135618 titled 'GENERATING ARTIFICIAL AGENTS FOR REALISTIC MOTION SIMULATION USING BROADCAST VIDEOS
Simplified Explanation
The abstract describes a patent application related to artificial intelligence agents generating more natural motion for simulated actors in visualizations such as video games or simulations. The AI agents use machine learning models like reinforcement learning to synthesize motion with enhanced realism, trained on broadcast video data. Techniques are employed to improve the quality of motion, such as considering joint motion and applying physics-based constraints on actors.
- AI agents generate natural motion for simulated actors in visualizations
- Machine learning models like reinforcement learning are used
- Trained on broadcast video data
- Techniques improve motion quality by considering joint motion and applying physics-based constraints
Potential Applications
The technology can be applied in various industries such as entertainment (video games, movies), virtual reality, training simulations, and robotics.
Problems Solved
1. Enhancing realism in motion synthesis for simulated actors 2. Reducing the need for costly and limited motion capture data
Benefits
1. Improved quality and lifelike motion in visualizations 2. Cost-effective training using widely available broadcast video data
Potential Commercial Applications
Optimizing motion in video games for a more immersive experience
Possible Prior Art
Prior art may include existing AI systems for motion synthesis in visualizations, motion capture technologies, and machine learning models for animation.
Unanswered Questions
How does this technology compare to traditional motion capture methods in terms of accuracy and cost-effectiveness?
The article does not provide a direct comparison between this AI-based approach and traditional motion capture methods.
What are the limitations of using broadcast video data for training AI agents in motion synthesis?
The article does not address the potential limitations or challenges of using broadcast video data for training AI agents.
Original Abstract Submitted
in various examples, artificial intelligence (ai) agents can be generated to synthesize more natural motion by simulated actors in various visualizations (such as video games or simulations). ai agents may employ one or more machine learning models and techniques, such as reinforcement learning, to enable synthesis of motion with enhanced realism. the ai agent can be trained based on widely-available broadcast video data, without the need for more costly and limited motion capture data. to account for the lower quality of such video data, various techniques can be employed, such as taking into account the motion of joints, and applying physics-based constraints on the actors, resulting in higher quality, more lifelike motion.
