Procedural Material Retrieval from Reference Images Using Color Histograms
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Solution Overview
Problem
Conventional techniques for authoring procedurally generated materials are time-consuming and inefficient, and machine learning approaches often fail to provide accurate color-matching in procedural material retrieval, leading to off-target results.
Innovation Solution
A content processing system that generates a histogram representation and color distribution of an input image, filters candidate materials using a vision language model for semantic similarity, and compares color distributions using a Wasserstein metric to identify procedurally generated materials that match the input image's color and semantic features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual authoring of procedural materials is used, then user control and customization are improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The system creates procedural materials by copying visual characteristics from reference images rather than requiring manual authoring. The neural network analyzes reference images and generates procedural materials that replicate their visual properties, eliminating the need for users to manually adjust parameters while maintaining creative control through image selection.
Solution Approach 2:
The patent replaces manual mechanical adjustment of procedural parameters with an automated neural network system. Instead of users manually tuning material parameters, the system uses deep learning models to automatically generate procedural materials from reference images, substituting human manual operations with intelligent automation.
2Ease of operation
If manual browsing of existing materials is used, then user control is maintained, but time consumption increases and creative capabilities are limited
Solution Approach 1:
The system copies visual characteristics from reference images to generate procedural materials, eliminating the need for users to manually browse through existing materials. The neural network efficiently generates relevant materials directly from image references, saving time while maintaining creative control.
Solution Approach 2:
The system performs preliminary analysis of reference images by the neural network before generating procedural materials. This preliminary action of analyzing and understanding the visual characteristics of reference images enables the system to directly generate appropriate materials without requiring users to manually search through large databases.
3Extent of automation
If conventional machine learning approaches are used for material retrieval, then automation is improved, but color-matching accuracy deteriorates
Solution Approach 1:
The patent changes the parameters used for material retrieval from conventional machine learning approaches to neural network-based visual analysis. The system uses neural networks to analyze color distributions, textures, and visual properties of reference images, enabling accurate color-matching while maintaining automation. The parameter transformation involves converting image data into procedural material parameters through neural network learning.
Data Source
AI summary
Techniques for reference image based material retrieval are described that support identification of procedural materials based on visual features of input images. A processing device, for instance, receives an input image that has a particular visual appearance. The processing device generates a histogram representation of the input image that represents a color prominence of the input image and generates a color distribution based on the color prominence. The processing device leverages a vision language model to filter candidate procedural materials by a semantic similarity to the input image. The processing device then identifies a procedural material that has a visual similarity to the particular visual appearance by comparing the color distribution for the input image to color distributions associated with the filtered candidate procedural materials. In this way, the techniques described herein support efficient retrieval of procedural materials based on color and on semantic features of the input image.