AI Character Asset Creation Through Attribute Decomposition
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Solution Overview
Problem
The development of video game characters is time-consuming, impacting the overall game development cycle and potentially delaying the release date due to the manual and iterative process of creating detailed visual representations.
Innovation Solution
A method utilizing generative artificial intelligence to iteratively generate and edit attributes of a target object, such as a character, through phases of input, decomposition, selection, editing, and blending, allowing for dynamic creation of multiple versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual creation methods are used to generate detailed visual representations of characters, then the quality and detail of the character can be improved, but the development time increases significantly
Solution Approach 1:
The patent segments the character creation process into distinct phases: initial generation using AI, attribute decomposition into separate components, individual attribute editing, and iterative refinement. This segmentation allows parallel processing of different character attributes and enables focused refinement without redoing entire character models, thus improving detail quality while reducing overall development time.
Solution Approach 2:
The system performs preliminary action by using AI to generate complete character models with all attributes initially populated. This preliminary generation provides a comprehensive base that can be selectively refined, eliminating the need to create detailed characters from scratch and significantly reducing the time required to achieve high-quality detailed representations.
2Manufacturing precision
If detailed representations are created manually, then the quality of the final character can be improved, but the overall game development cycle is delayed
Solution Approach 1:
The system implements self-service by enabling automated AI generation of character models and their attributes. The AI autonomously creates detailed character representations with multiple attributes (appearance, equipment, statistics, etc.), which can then be selectively refined. This self-service capability eliminates bottlenecks in the development pipeline and maintains high productivity while achieving detailed quality standards.
Solution Approach 2:
The patent applies parameter changes by allowing independent adjustment of individual character attributes (e.g., appearance, equipment, stats) without affecting other parameters. This enables efficient optimization of specific character aspects while maintaining overall development speed, as changes can be made locally rather than requiring complete regeneration of the character model.
3Manufacturing precision
If an iterative process is used to refine character details, then the final character quality can be improved, but the time required for each iteration increases
Solution Approach 1:
The iterative refinement process is segmented into attribute-specific operations. Each iteration focuses on refining specific character attributes (appearance, equipment, statistics) independently rather than refining the entire character model. This segmentation reduces the computational and time requirements for each iteration while maintaining the ability to achieve high-detail quality through multiple focused refinement cycles.
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
Facilitates rapid and creative generation of visual assets by enabling users to select, edit, and tune AI-generated attributes, reducing development time and enhancing the efficiency of character creation in video games.
Implementation Method 1
generating a plurality of images of the target object using an image generation artificial intelligence system configured for implementing latent diffusion based on the one or more inputs
Data Source
AI summary
A method including collecting one or more inputs, each of which describes a target object. The method including generating a plurality of images of the target object using an image generation artificial intelligence system configured for implementing latent diffusion based on the one or more inputs. The method including decomposing the target object into a first plurality of attributes based on the plurality of images of the target object, wherein each of the plurality of attributes includes one or more variations. The method including receiving selection of one or more of a plurality of variations of the plurality of attributes. The method including blending the one or more of the plurality of variations of the plurality of attributes that have been selected into one or more options of the target object.