Nvidia corporation (20240249458). LEARNING DIRECTABLE VIRTUAL AGENTS THROUGH CONDITIONAL ADVERSARIAL LATENT MODELS simplified abstract
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LEARNING DIRECTABLE VIRTUAL AGENTS THROUGH CONDITIONAL ADVERSARIAL LATENT MODELS
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LEARNING DIRECTABLE VIRTUAL AGENTS THROUGH CONDITIONAL ADVERSARIAL LATENT MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240249458 titled 'LEARNING DIRECTABLE VIRTUAL AGENTS THROUGH CONDITIONAL ADVERSARIAL LATENT MODELS
The abstract describes a Conditional Adversarial Latent Model (CALM) process that can generate new movements for an agent by using a set of original reference movements.
- The CALM process can create a library of new movements for virtual characters, animals, or objects.
- An encoder maps requested movements onto a latent space, while a low-level policy generates joint movements for the agent.
- A conditional discriminator provides feedback to the low-level policy to produce stationary distributions over the agent's states.
- A high-level policy controls macro movements, such as direction in the environment, using a reward or finite-state machine function.
Potential Applications: - Animation and gaming industries for creating realistic and diverse movements for characters. - Robotics for generating complex and varied motions for robots in different environments.
Problems Solved: - Limited movement variety in virtual characters and robots. - Difficulty in creating natural and diverse motions for agents.
Benefits: - Increased realism and diversity in movements for virtual characters and robots. - Enhanced user experience in animations, games, and robotics applications.
Commercial Applications: Title: "Enhancing Motion Diversity in Virtual Characters and Robots" This technology can be used in the animation, gaming, and robotics industries to improve the quality and variety of movements in virtual characters and robots, leading to more engaging and realistic user experiences.
Questions about the technology: 1. How does the CALM process improve the diversity of movements in virtual characters and robots? 2. What are the potential limitations or challenges in implementing this technology in real-world applications?
Original Abstract Submitted
a conditional adversarial latent model (calm) process can be used to generate reference motions from a set of original reference movements to create a library of new movements for an agent. the agent can be a virtual representation various types of characters, animals, or objects. the calm process can receive a set of reference movements and a requested movement. an encoder can be used to map the requested movement onto a latent space. a low-level policy can be employed to produce a series of latent space joint movements for the agent. a conditional discriminator can be used to provide feedback to the low-level policy to produce stationary distributions over the states of the agent. a high-level policy can be employed to provide a macro movement control over the low-level policy movements, such as providing direction in the environment. the high-level policy can utilize a reward or a finite-state machine function.
