Voice-Driven 3D Asset Modification for Selective Simulation Updates
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Solution Overview
Problem
Existing methods for creating and modifying computer game assets are inefficient and lack the ability to accurately convert 2D images to 3D assets using natural language inputs.
Innovation Solution
A system that utilizes neural networks and AI engines to convert voice or image inputs into 2D images, which are then transformed into 3D assets, allowing for selective modification of specific parts while maintaining consistency with user descriptions and physics constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to create and modify computer game assets, then the process is time-consuming and inefficient, but the new neural network-based method can accurately convert 2D images to 3D assets and modify specific parts selectively
Solution Approach 1:
The patent replaces traditional manual asset creation and modification processes with neural network-based automated systems. The neural networks automatically convert 2D images to 3D assets and apply selective modifications based on natural language descriptions, eliminating the need for manual 3D modeling and rendering operations.
Solution Approach 2:
The patent introduces neural networks as intermediary components between 2D image inputs and 3D asset outputs. These neural networks act as mediators that automatically translate 2D visual data into 3D representations and apply modifications, serving as the computational bridge between input and output without requiring manual intervention.
2Manufacturing precision
If manual methods are used to modify specific parts of assets, then precision can be maintained, but the process is time-consuming and lacks the ability to accurately convert 2D to 3D
Solution Approach 1:
The patent replaces manual 2D to 3D conversion and modification processes with neural network-based automated systems. The neural networks automatically translate 2D images into 3D assets with high accuracy and can selectively modify specific parts based on natural language descriptions, eliminating the need for manual 3D modeling and rendering operations.
Solution Approach 2:
The patent performs preliminary conversion of 2D images to 3D assets using neural networks before any modification process begins. This preliminary action establishes the 3D representation and identifies modifiable regions in advance, enabling subsequent selective modifications to be applied efficiently without requiring time-consuming manual conversion and analysis.
3Adaptability or versatility
If comprehensive asset modification is performed, then all aspects of the asset can be updated, but the process is inefficient and cannot maintain consistency with user descriptions
Solution Approach 1:
The patent segments the asset modification process into specific modifiable regions or parts rather than treating the entire asset as a monolithic unit. The system identifies and isolates specific regions that need modification based on natural language descriptions, allowing targeted changes to individual parts while maintaining the rest of the asset unchanged.
Solution Approach 2:
The patent applies different modification qualities and operations to different local regions of the asset based on user descriptions. Each modifiable region receives tailored modification treatment appropriate to its specific characteristics and the user's requirements, rather than applying uniform modifications across the entire asset.
Data Source
AI summary
A computer simulation object such as a chair is described by voice or photo input to render a 2D image. Machine learning may be used to convert voice input to the 2D image. The 2D image is converted to a 3D asset and the 3D asset or portions thereof are used in the computer simulation, such as a computer game, as the object such as a chair.