AI Virtual Location Creation With 3D Scene Generation
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Solution Overview
Problem
Creating immersive virtual locations is a time-consuming and resource-intensive process, and existing methods fail to provide customizable, efficient, and shareable environments that can integrate with real-world physics and adapt to the unique physical rules of different environments, and existing methods lack effective solutions for easily sharing these environments among creators and users.
Innovation Solution
A system that integrates immersive environments that leverage generative artificial intelligence to automate the creation of immersive environments, and shareable environments that integrate with real-time physics and adapt to the unique physical rules of immersive environments, and shareable environments that integrate with real-time physics and adapt to the unique physical rules of different environments, and existing methods for creating these environments may not be sufficiently immersive or customizable, or efficient in terms of time and resources required for creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual methods are used to create immersive virtual locations, then customization and control over environment details are improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training generative AI models on extensive datasets of virtual environments, character models, and physics simulations. This pre-computation enables the models to quickly generate customized environments without requiring manual creation during actual use, thus reducing time consumption while maintaining high customization capability
Solution Approach 2:
The patent introduces generative AI models as an intermediary between the user's customization requirements and the actual virtual environment creation. The AI model translates high-level user specifications into detailed environment configurations, automatically handling the complex task of generating assets, placing objects, and configuring physics parameters, thereby eliminating the need for time-consuming manual creation while preserving full customization
2Reliability
If existing virtual environment creation methods are used, then basic environments can be generated, but they lack sufficient immersion and realism for effective learning and training
Solution Approach 1:
The system combines multiple AI-generated components (geometry, textures, materials, lighting, physics parameters) into a composite virtual environment that achieves high immersion quality. Each component is generated by specialized AI models trained on realistic data, and their integration creates a cohesive, immersive experience that surpasses traditional methods while maintaining efficient automated creation
Solution Approach 2:
The patent employs parameter changes by adjusting physics simulation parameters, material properties, and environmental conditions to enhance realism and immersion. The AI model dynamically optimizes these parameters based on the specific training or simulation requirements, enabling high-quality immersive environments to be generated automatically without manual tuning, thus improving both immersion quality and creation efficiency
3Adaptability or versatility
If traditional virtual environment systems are used, then environments can be created, but they cannot accurately represent or adapt to unique physical rules of different virtual environments
Solution Approach 1:
The system implements dynamics by enabling physics parameters and rules to be dynamically adjusted and adapted to different virtual environments. The AI model learns the unique physical characteristics of each target environment and automatically configures the physics simulation accordingly, allowing the same system to handle diverse physics scenarios without requiring complex manual reconfiguration or multiple specialized systems
4Adaptability or versatility
If manual creation methods are used, then environments can be tailored to specific requirements, but the process is not repeatable and lacks consistency
Solution Approach 1:
The system incorporates feedback mechanisms where the AI model evaluates generated environments against the specified requirements and iteratively refines the output. This feedback loop ensures that each generated environment consistently meets the tailoring requirements while maintaining repeatability, as the AI model applies the same learned patterns and optimization criteria across different generation tasks, producing consistent results for similar input specifications
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system efficiently creates customizable and shareable immersive virtual environments, overcoming real-world physics limitations and reducing time and resource requirements, while providing realistic training and simulation experiences.
Implementation Method 1
transmitted to a text-to-video generative artificial intelligence model. A video of the virtual location is received from the text-to-video generative artificial intelligence model
Implementation Method 2
converted into a three-dimensional scene using a volume rendering technique associated with Gaussian splatting
Data Source
AI summary
A system associated with an immersive experience framework may include an immersive virtual location data store containing information about a plurality of three-dimensional scenes (with each scene being associated with an immersive virtual location). An immersive virtual location tool may receive, from a creator, an immersive virtual location request (e.g., including an environment description). A request prompt is created based on the environment description and transmitted to a text-to-video generative AI model. A video of the virtual location is received from the generative AI model and converted into a three-dimensional scene using a volume rendering technique. Information about the scene is stored in the immersive virtual location data store and a user can interact with the scene using a substantially real-time experience interaction engine. In some embodiments, a JSON file describing the scene is directly generated using a LLM without creating the video.