Conditional Adversarial Latent Control for Directable Virtual Agents
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Solution Overview
Problem
Creating realistic and diverse behaviors for virtual agents in virtual environments is challenging due to the complexity of human motion, which disrupts immersive experiences if virtual agents do not respond naturally to user input.
Innovation Solution
A conditional adversarial latent model (CALM) is employed to learn a representation of human movement, enabling virtual agents to adapt to changing environments and user inputs by encoding raw motion capture recordings into a latent representation and decoding them into physically simulated character movements, allowing for directable control and diverse behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control models are used to generate complex and realistic behaviors for virtual agents, then the immersive experience is improved, but the device complexity increases
Solution Approach 1:
The control model is segmented into multiple components: a motion encoder that processes reference motions, a latent space representation layer, and a policy network that generates control actions. This segmentation allows each component to specialize in specific tasks, reducing overall system complexity while maintaining realistic behavior generation capabilities.
Solution Approach 2:
A latent space representation is introduced as an intermediary between the motion encoder and the policy network. This latent space acts as a compressed representation of motion characteristics, enabling the system to generalize from reference motions without requiring complex direct mapping, thus reducing model complexity while preserving behavioral realism.
2Adaptability or versatility
If virtual agents adapt to different environments and user inputs, then the adaptability is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system incorporates feedback mechanisms where the policy network observes the agent's current state and environment conditions, then adjusts control actions accordingly. This feedback loop enables adaptive behavior to different environments and user inputs while providing measurable control signals that can be monitored and evaluated.
Solution Approach 2:
The motion encoder transforms diverse reference motions into a standardized latent space representation, effectively changing the parameter space to a manageable form. This parameter transformation allows the system to handle various motion types uniformly, reducing the difficulty of detecting and measuring motion complexity while maintaining adaptability.
Data Source
AI summary
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.