Digital Twin Scene Augmentation for Diverse Synthetic AI Data
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Solution Overview
Problem
Existing AI training systems face challenges in generating high-quality, diverse training data efficiently, as traditional methods are resource-intensive and often fail to produce realistic and varied simulations, leading to overfitting and inadequate performance in real-world scenarios.
Innovation Solution
The use of domain randomization techniques, including post-processing methods, to generate synthetic training data by randomly varying scene parameters and adding objects within 3D environments, using AI systems like LLMs and VLMs to create photorealistic images and videos, and applying augmentation engines to enhance diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to generate training data, then the data can be obtained, but the process is resource-intensive and time-consuming
Solution Approach 1:
The patent creates synthetic training data by copying and rendering 3D environments rather than capturing real-world data. Virtual agents navigate virtual scenes, and their perspectives are rendered as images, providing unlimited copies of training data without the resource constraints of physical data collection
Solution Approach 2:
The system varies multiple parameters including camera position, lighting conditions, object positions, and environmental characteristics to generate diverse training data. This parameter randomization allows efficient exploration of the training space without repeated resource consumption
2Reliability
If simulated training data is generated, then resource consumption is reduced, but the quality and realism of the generated images may be insufficient
Solution Approach 1:
The system dynamically adjusts lighting models, camera parameters, and scene configurations during rendering to achieve photorealistic results. The virtual agents perform actions that dynamically interact with the environment, creating realistic motion and interaction patterns
Solution Approach 2:
The patent replaces physical camera systems and real-world capture mechanisms with computational rendering engines. This substitution eliminates the mechanical constraints of physical data collection while maintaining high image quality through advanced rendering techniques
3Adaptability or versatility
If diverse training data is generated, then model performance improves, but the complexity of generating varied simulations increases
Solution Approach 1:
The training environment is segmented into modular components including virtual agents, objects, and scene elements that can be independently configured and combined. This modular approach enables diverse training scenarios through simple parameter changes rather than complex system reconfigurations
Solution Approach 2:
The simulation framework is designed as a universal platform where the same base system can generate diverse training data by varying parameters. The virtual agents and environments serve multiple functions across different training tasks, reducing the need for specialized systems for each scenario
Data Source
AI summary
Approaches presented herein may be used to generate synthetic image data, which may be used to train a variety of artificial intelligence (AI) systems. Synthetic image data may be produced by augmenting three-dimensional (3D) scene information as a post-processing step, for example after simulation. The augmented scenes may change a variety of parameters associated with 3D assets that are injected into the scene after initial rendering, thereby increasing diversity of a dataset.