Synthetic Data Generation for Dynamic Object Compositing
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Solution Overview
Problem
Current virtual and augmented reality systems require significant developer input for creating augmented reality environments, which is time-consuming and labor-intensive, especially when compositing dynamic objects into scenes.
Innovation Solution
Generating synthetic training data to train a machine learning model that can automatically augment images or videos with dynamic objects by simulating their movement within 3D environments using physics simulators and rendering depth and surface normal maps from multiple viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If developers code augmented reality environments in a frame by frame manner, then the augmented reality systems can be created with precise control, but the process becomes very time consuming and labor intensive
Solution Approach 1:
The system pre-simulates dynamic object movements and interactions in a virtual environment to generate training data before actual augmented reality content creation. This preliminary simulation creates a library of realistic movement patterns, collision responses, and environmental interactions that can be directly applied during frame-by-frame coding, significantly reducing the time required while maintaining precision.
Solution Approach 2:
The system creates synthetic training data by copying and simulating real-world physics and environmental interactions in a virtual model. These synthetic datasets replicate realistic object behaviors, lighting conditions, and scene dynamics, allowing the machine learning model to learn from numerous simulated examples without requiring manual frame-by-frame coding for each scenario.
2Adaptability or versatility
If multiple viewpoints and environments are simulated to improve training data quality, then the machine learning model achieves better generalization, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary simulations across multiple viewpoints and environments to generate comprehensive training data before model training begins. By pre-computing diverse scenarios including different camera angles, lighting conditions, and environmental configurations, the system creates a robust dataset that enables the model to generalize well without requiring extensive computational resources during the actual training phase.
Data Source
AI summary
This application relates generally to augmenting images and videos with dynamic object compositing, and more specifically, to generating synthetic training data to train a machine learning model to automatically augment an image or video with a dynamic object. The synthetic training data may contain multiple data points from thousands of simulated dynamic object movements within a virtual environment. Based on the synthetic training data, the machine learning model may determine the movement of a new dynamic object within new virtual environment.