Machine Learning Graphics Asset Modification
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Solution Overview
Problem
The creation and development of content for computer-generated environments in video games is a laborious process due to the increasing size, complexity, and realism of games, making asset creation time-consuming for developers.
Innovation Solution
A method using machine learning to generate images based on text data and modify initial graphics assets for video games, including model data and texture data, to create a bespoke visual appearance for the game.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If developers manually create virtual objects by specifying graphics assets, model data, and texture data, then the quality and customization of game content can be maintained, but the development time and labor required increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of asset creation with an automated machine learning system. The ML model generates graphics assets, model data, and texture data automatically based on text inputs, eliminating the need for developers to manually specify each asset while maintaining quality standards through algorithmic generation and consistency enforcement.
Solution Approach 2:
The system enables self-service content generation where the machine learning model autonomously creates and modifies graphics assets without requiring continuous human intervention. Developers provide high-level text descriptions or seed assets, and the system handles the detailed asset creation, modification, and optimization processes automatically.
2Reliability
If developers create assets for increasingly complex and realistic video games, then the realism and detail of the game environment improve, but the time consumption for asset creation increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on large datasets of realistic graphics assets before actual game development. This pre-training enables the models to generate high-quality, realistic assets directly during development without requiring manual creation of each individual asset, significantly improving productivity while maintaining realism.
Solution Approach 2:
The patent utilizes parameter changes in machine learning model configurations to control the level of realism and detail in generated assets. By adjusting model parameters such as resolution, complexity levels, and style transfer settings, the system can efficiently generate assets at varying degrees of realism to match different game requirements without proportionally increasing development time.
3Adaptability or versatility
If custom graphics assets are created for each virtual object, then the visual appearance and customization options improve, but the complexity of the content creation process increases
Solution Approach 1:
The machine learning system serves multiple functions within a single unified platform: it generates new graphics assets, modifies existing assets, applies style transfers, and ensures consistency across different virtual objects. This multi-functional approach provides extensive customization capabilities while simplifying the overall creation process by consolidating multiple tools and workflows into one automated system.
Data Source
AI summary
A computer-implemented method comprises obtaining a set of initial graphics assets for a video game, an initial graphics asset comprising at least initial model data for an object, obtaining text data from one or more data sources, inputting at least some of the text data to a machine learning model, generating one or more respective images in dependence on the text data using the machine learning model, and modifying the set of initial graphics assets for the video game in dependence upon one or more of the respective images generated using the machine learning model.