Character Interaction Control Policies Across Different Morphologies
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Solution Overview
Problem
Existing methodologies for character animation in virtual environments struggle to generalize motion control policies across different character morphologies and interaction scenarios, requiring manual adjustments and lacking dynamic adaptability.
Innovation Solution
A neural network pipeline processes spatial representations of virtual environments using graph neural networks and reinforcement learning to adapt character interactions to varying morphologies and scenarios, generating control signals through a feedforward neural network and refining actions with a reward function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If predefined motion capture data or handcrafted animations are used, then character animation quality is improved, but adaptability to different character morphologies and interaction scenarios deteriorates
Solution Approach 1:
The patent replaces traditional mechanical motion capture and handcrafted animation systems with a neural network-based policy system. The policy takes character morphology and interaction scenario as input and generates appropriate motion control signals, enabling automatic adaptation without manual adjustment for each character type or scenario.
Solution Approach 2:
The system changes the parameter representation from fixed motion capture data to dynamic policy outputs that adapt to varying character morphology parameters and interaction scenario parameters. The policy learns to map from morphology/scenario parameters to appropriate motion control parameters, enabling continuous adaptation across diverse character types.
2Measurement precision
If manual adjustments are made for different character types, then motion control accuracy is improved, but system complexity and time consumption increase
Solution Approach 1:
The policy system performs self-adjustment for different character morphologies and interaction scenarios without requiring manual intervention. The neural network policy automatically adapts its output based on the input character morphology and scenario parameters, eliminating the need for manual tuning while maintaining high motion control accuracy.
Solution Approach 2:
A single universal policy handles multiple character types and interaction scenarios that would traditionally require separate motion capture sessions or handcrafted animations for each. The policy generalizes across diverse characters and scenarios, reducing system complexity while maintaining accuracy through its ability to adapt to any input configuration.
3Reliability
If separate control policies are created for different interactions, then interaction specificity is improved, but computational resources and training time increase
Solution Approach 1:
The patent merges multiple interaction-specific control policies into a single unified policy that handles diverse interactions through conditional processing. The policy takes interaction scenario as input and dynamically adjusts its behavior accordingly, combining the functionality of multiple specialized policies into one resource-efficient system that maintains interaction specificity.
Data Source
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AI summary
Some implementations relate to methods, systems, and computer-readable media for adapting simulated character interactions to different morphologies and interaction scenarios. The system accesses a graph representing a control policy for a simulated character's movements in a virtual environment. This graph undergoes encoding and processing through a graph neural network to generate latent embeddings for the graph. A fixed-length latent vector is determined from the latent embeddings. This vector is input to a feedforward neural network, generating control signals for the character's actions. Through a reinforcement learning loop, the character's motions are continuously refined by iteratively adjusting the graph based on evaluating the actions of the simulated character via a reward function, adapting the control policy to different character morphologies and/or interaction scenarios.