Semantic Asset Generator Control With Embedding-Based Query Retrieval
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Solution Overview
Problem
Modern asset generators for games, virtual worlds, and design applications have complex user interfaces and intricate control flows, requiring a significant learning curve due to numerous parameters and parameter settings, making it difficult for users to produce assets that match complex constraints and artistic intents.
Innovation Solution
A system that pre-computes and stores asset generator outputs with associated parameter settings, allowing users to query and interact with assets using natural language and image inputs, enabling fine-grained control and intuitive asset generation through a unified interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If asset generators provide numerous parameters and detailed control options to achieve precise asset generation, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary system that includes pre-computed asset examples, parameter settings, and a matching engine. This intermediary layer sits between the user and the complex asset generator, translating simple user queries into precise asset generation commands without exposing users to the underlying complexity of numerous parameters and control options.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing numerous asset examples with their corresponding parameter settings before user interaction. This pre-computation creates a library of ready-to-use assets that can be quickly matched to user needs, eliminating the need for users to navigate complex parameter settings during actual asset generation tasks.
2Adaptability or versatility
If asset generators provide numerous parameters and detailed control options, then asset generation capability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent segments the asset generation system into distinct components: a pre-computed asset library, a query interface, and a matching engine. This segmentation allows users to interact with only the simple query interface while the other segments handle the complex asset generation capabilities, effectively hiding complexity from users.
Solution Approach 2:
The matching engine acts as an intermediary that translates simple user queries into precise asset selection commands. It bridges the gap between the simple user interface and the complex asset generation system, enabling users to access versatile asset generation capabilities without dealing with complex controls.
3Manufacturing precision
If asset generators include intricate control flows and multiple settings, then asset generation precision is improved, but learning curve increases
Solution Approach 1:
The system creates copies of pre-computed assets and their associated parameter settings, storing them in a accessible library. Users can query and retrieve these copied assets without needing to understand or configure the original complex generation processes, thereby maintaining precision while reducing learning requirements.
Solution Approach 2:
The matching engine serves as an intermediary that handles the translation from simple user queries to precise asset retrieval, shielding users from the intricate control flows and multiple settings that would otherwise require extensive learning.
4Productivity
If the system pre-computes and stores numerous asset examples with parameter settings, then productivity is improved, but loss of time for pre-computation occurs
Solution Approach 1:
The system performs asset generation and parameter configuration in advance, storing the results in a pre-computed library. This preliminary action shifts the computational workload from runtime (when users need assets) to offline preparation time, significantly improving productivity during actual asset retrieval operations.
Data Source
AI summary
System and method for determining sets of parameter values for asset generators, generating assets using the asset generators and the sets of parameter values, generating asset embeddings for asset representations, and storing the asset embeddings and one or more of the generated assets or asset generator information associated with the asset generators. The system receives query inputs and uses them to computes a query embedding. The system retrieves a set of asset embeddings matching the query embedding, each asset embedding being associated with a corresponding asset and/or asset generator information that includes an asset generator ID and/or a set of parameter values used to generate the asset. The system can display, in a user interface (UI), retrieved assets and/or asset generator information for further user-driven asset editing and/or asset regeneration. Asset representations and query inputs can span multiple modalities, such as natural language (NL) descriptions, images, and so forth.