ML Human Character Simulation with Physics-Based Motion
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Solution Overview
Problem
Conventional methods for simulating human character movements in complex environments lack realism, failing to account for diverse characteristics, social interactions, and physics-based interactions with terrain and obstacles, resulting in unrealistic and unresponsive simulations.
Innovation Solution
The development of a system using a machine learning model that generates and updates simulated human characters with diverse characteristics, enabling them to follow trajectories and interact realistically within complex environments by incorporating reinforcement learning, motion symmetry loss, and social awareness, allowing for terrain traversal and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to simulate human character movements, then the simulation can be computationally simple and fast, but the realism and diversity of movements are poor
Solution Approach 1:
The patent replaces conventional physics-based mechanical simulation systems with a machine learning model (neural network) that learns movement patterns from motion capture data. This substitution enables realistic and diverse human character movements while maintaining computational efficiency, as the ML model directly predicts movements without requiring complex real-time physics calculations.
Solution Approach 2:
The system performs preliminary action by pre-processing motion capture data and training the machine learning model offline before deployment. Motion capture sequences are processed to create training datasets, and the model is trained in advance to learn realistic movement patterns. This allows the simulation to use pre-learned knowledge during runtime, improving both realism and computational efficiency.
2Reliability
If each simulated human character is controlled independently without awareness of others, then the control system is simple, but the interactions and collisions are unrealistic
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it controls individual character movements, predicts interactions with other characters, and responds to environmental obstacles. This multi-functionality enables realistic social interactions and collisions without requiring separate specialized systems for each function, thus improving realism while managing complexity.
Solution Approach 2:
The system implements feedback by having simulated human characters aware of and responsive to other characters in the environment. The ML model processes information about nearby characters and adjusts movements accordingly, enabling realistic interactions and collision avoidance. This feedback mechanism creates emergent social behaviors without requiring explicit programming of interaction rules.
3Adaptability or versatility
If the machine learning model is trained only on basic movement data, then the training process is fast and simple, but the diversity of characteristics and behaviors is limited
Solution Approach 1:
The system achieves diversity by varying multiple parameters during training and simulation, including body proportions, gender, age, and motion capture data sources. By changing these parameters and training the model on diverse datasets, the system generates characters with varied characteristics and behaviors. This parameter-based approach enables adaptability without requiring fundamentally different models for each character type.
Solution Approach 2:
The training process is segmented into multiple stages: data collection from various motion capture sources, preprocessing and annotation of motion sequences, model training with different hyperparameters, and validation. This segmentation allows systematic exploration of diverse character characteristics while managing training complexity through organized, modular processing steps.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to generating a simulated environment and update a machine learning model to move each of a plurality of human characters having a plurality of body shapes, to follow a corresponding trajectory within the simulated environment as conditioned on a respective body shape. The simulated human characters can have diverse characteristics (such as gender, body proportions, body shape, and so on) as observed in real-life crowds. A machine learning model can determine an action for a human character in a simulated environment, based at least on a humanoid state, a body shape, and task-related features. The task-related features can include an environmental feature and a trajectory.