Procedural Media Generation via Neural Graph Synthesis
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Solution Overview
Problem
Manually designing a directed graph for media assets, such as textures in 3D modeling, is time-consuming and requires expertise, limiting the ability of non-expert users to generate high-quality media assets.
Innovation Solution
A procedural media generation system using machine-learning based techniques to automatically generate parameterized nodes and directed edges, enabling the creation of procedural media generators that can produce high-quality media assets without manual programming, through a node generation network, parameter generation network, and edge generation network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual graph design is used to create procedural media generators, then the quality and control of media assets are improved, but the time consumption and complexity increase significantly
Solution Approach 1:
The system enables automatic generation of procedural media generators through machine learning models that self-configure the directed graphs without requiring manual design. The neural networks automatically determine node types, parameters, and edge connections based on desired output characteristics, allowing the system to serve itself rather than requiring expert manual configuration.
Solution Approach 2:
The patent replaces the manual mechanical process of graph design with an automated computational system. Machine learning models substitute for human experts in determining the structure and parameters of procedural graphs, transforming a manual creative process into an automated algorithmic one that maintains quality while reducing time investment.
2Manufacturing precision
If manual graph design is used to ensure expertise and control, then the quality of media assets is improved, but the ease of operation decreases for non-expert users
Solution Approach 1:
The patent introduces machine learning models as intermediaries between user requirements and procedural graph generation. These models act as mediators that translate high-level user specifications into complex graph structures without requiring users to understand the underlying technical details, thus maintaining quality while improving ease of operation.
Solution Approach 2:
The system performs automatic configuration of procedural graphs through self-service mechanisms where the machine learning models independently determine optimal graph structures based on desired outcomes, eliminating the need for user expertise in graph design while maintaining high quality results.
3Productivity
If automatic generation is used to improve productivity and ease of use, then the efficiency increases, but the manufacturing precision and quality control may decrease
Solution Approach 1:
The patent replaces manual quality control processes with automated machine learning-based generation and validation systems. The neural networks are trained to produce high-quality outputs and include built-in validation mechanisms that automatically ensure quality standards are met, maintaining manufacturing precision while enabling automatic generation at scale.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning models continuously learn from generated results and adjust their parameters to maintain or improve quality. Validation feedback loops ensure that automatically generated procedural graphs meet quality standards, allowing high productivity without sacrificing manufacturing precision.
Data Source
AI summary
Aspects of a system and method for procedural media generation include generating a sequence of operator types using a node generation network; generating a sequence of operator parameters for each operator type of the sequence of operator types using a parameter generation network; generating a sequence of directed edges based on the sequence of operator types using an edge generation network; combining the sequence of operator types, the sequence of operator parameters, and the sequence of directed edges to obtain a procedural media generator, wherein each node of the procedural media generator comprises an operator that includes an operator type from the sequence of operator types, a corresponding sequence of operator parameters, and an input connection or an output connection from the sequence of directed edges that connects the node to another node of the procedural media generator; and generating a media asset using the procedural media generator.