Neural Network Material Map Generation for Realistic Rendering
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Solution Overview
Problem
Existing techniques for generating material maps in digital graphical environments fail to produce highly realistic appearances of graphical objects, as they rely on naive reduction methods that do not preserve visual features and are not responsive to the texture's appearance, leading to suboptimal rendering quality.
Innovation Solution
An appearance-responsive material map generation system utilizing a neural network that identifies features contributing to a realistic appearance, generating a set of material maps with varying resolutions, sizes, and data characteristics, arranged as an inconsistent pyramid, to create a more realistic and efficient rendering of graphical objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If precalculated material maps are generated using naive reduction methods, then computing resources are reduced, but visual features and rendering quality are not preserved
Solution Approach 1:
The patent changes the parameters of material map generation by using a neural network to learn and preserve appearance characteristics at different resolutions. Instead of uniform naive reduction, the system adapts the material map generation process based on appearance importance, transforming how material maps are created across different scales while maintaining visual fidelity where it matters most
Solution Approach 2:
The patent applies preliminary action by precalculating appearance-responsive material maps at multiple resolutions before runtime. The neural network is trained offline to learn the relationship between high-resolution and low-resolution material maps, enabling fast rendering during gameplay without sacrificing appearance quality. This preliminary preparation resolves the contradiction between computing resources and rendering quality
2Speed
If material maps are reduced in size and detail sequentially, then rendering speed is improved, but visual features of source material data are not preserved
Solution Approach 1:
The patent transforms the material map reduction process by changing how resolution parameters are handled. Instead of uniform sequential reduction that loses visual features, the system uses appearance-responsive parameters that adaptively preserve important visual characteristics. The neural network learns which visual features are critical and maintains them across different resolution levels, enabling fast rendering without information loss
Solution Approach 2:
The patent applies local quality by making different regions of material maps have different levels of detail based on appearance importance. Critical visual features receive higher preservation priority while less important areas can be more aggressively downsampled. This localized approach to quality preservation enables rendering speed improvement without losing essential visual information
3Loss of time
If precalculated material maps are generated using contemporary techniques, then processing time is reduced, but appearance responsiveness and realism are compromised
Solution Approach 1:
The patent resolves this contradiction by performing preliminary action during an offline training phase. The neural network is trained in advance on pairs of high-resolution and low-resolution material maps to learn appearance preservation. This preliminary training enables the system to quickly generate appearance-responsive material maps at runtime without compromising realism, as the complex processing has already been optimized beforehand
Solution Approach 2:
The patent substitutes the traditional mechanical approach of uniform downsampling with a neural network-based system. Instead of relying on conventional resizing algorithms that sacrifice appearance quality, the system uses learned transformations that preserve visual realism. This substitution replaces rigid mechanical reduction with adaptive intelligent processing that maintains appearance responsiveness
Data Source
AI summary
An appearance-responsive material map generation system generates a set of material maps based on the appearance of a material depicted in the source material data. A neural network included in the appearance-responsive material map generation system is trained to identify features of particular source material data, such as features that contribute to a highly realistic appearance of a graphical object rendered with the material depicted in the source material data. In some cases, the trained neural network receives source material data that includes at least one source material map. Based on the features that are identified for the particular source material data, the appearance-responsive material map generation system creates a respective set of appearance-responsive material maps for the particular source material data. In some cases, the appearance-responsive rendering map set is arranged as an inconsistent pyramid of material maps.