Semantic Randomization for AI Training Scene Generation
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Solution Overview
Problem
Conventional approaches for synthetically generating training images for artificial intelligence models fail to incorporate real-world characteristics, leading to poor generalization of AI models to real-world images or video.
Innovation Solution
The system generates realistic and diverse simulated scenes with people by using semantic layers to vary textures, colors, materials, and patterns, and incorporates physical simulations such as gravity and collisions to enhance realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches are used to generate synthetic training images, then the generation process is simple and fast, but the images lack realism and fail to incorporate real-world characteristics
Solution Approach 1:
The generation process is divided into distinct stages: scene graph creation, semantic layer assignment, physical simulation, and rendering. Each stage handles specific aspects of image generation independently, allowing complexity to be managed systematically while achieving photorealistic results through coordinated execution of these segmented processes
Solution Approach 2:
Scene graphs serve as an intermediary data structure that bridges the gap between simple synthetic generation and complex real-world realism. The scene graphs encode semantic information, physical properties, and spatial relationships, enabling the system to generate realistic images without requiring direct access to real-world data during the generation process
2Adaptability or versatility
If semantic layers with randomization are applied to models, then diversity and variation in synthetic images increase, but the computational sampling and selection process becomes more complex
Solution Approach 1:
The system varies multiple parameters simultaneously across semantic layers including texture selection, color properties, material characteristics, and pattern types. By randomizing these parameters according to configured distributions, the system generates diverse images efficiently without requiring complex procedural generation algorithms for each individual parameter
Solution Approach 2:
The semantic layer framework provides a universal approach that can be applied to any 3D model or scene element. The same randomization and sampling mechanisms work across different object types, materials, and environmental conditions, eliminating the need for specialized generation processes for each specific case
3Reliability
If physical simulations are performed on objects in the environment, then realism of object interactions improves, but the computational time and processing requirements increase
Solution Approach 1:
Physical properties such as gravity, collision responses, and object interactions are pre-configured in the scene graphs before rendering. This preliminary setup allows the rendering engine to apply pre-determined physical behaviors directly during image generation without performing complex real-time physics simulations, significantly reducing computational time while maintaining realism
Solution Approach 2:
Instead of performing actual physical simulations during rendering, the system uses pre-computed or pre-configured physical interaction data stored in the scene graphs. These copied physical properties are applied directly to generate realistic object interactions without the computational overhead of running full physics engines during the image generation process
Data Source
AI summary
In various examples, systems and methods are disclosed relating to generating realistic and diverse simulated scenes of people for updating/training artificial intelligence models. A configuration file can be received that that specifies randomization for a semantic layer of a model for a scene. A distribution can be sampled according to the randomization to select data for the semantic layer of the model. The scene can be generated to include the model having the data selected for the semantic layer. The scene, including the model, can be rendered to generate an image for updating a neural network.