Procedural Material Generation From Reference Images

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Solution Overview

Problem

Conventional techniques for procedurally generating materials are time-consuming, inefficient, and limited by user experience, often requiring manual authoring or random generation with limited diversity, and fail to accurately replicate the visual properties of reference digital content.

Innovation Solution

A content processing system uses a machine learning model trained on a dataset of training pairs to generate procedural models based on input images, leveraging node, edge, and parameter transformers to automatically create node graphs that replicate the visual appearance of input images, including features like texture and pattern.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional manual techniques are used to author procedural materials, then user control is high, but the process is time-consuming and inefficient

Engineering Contradiction:

Improveuser controlVSAvoidgeneration efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

A machine learning model is introduced as an intermediary between the user's visual reference and the procedural material generation. The model automatically translates visual properties from reference images into procedural parameters, eliminating the need for manual adjustment while preserving user intent through the visual reference input.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables self-service procedural material generation by automatically analyzing reference images and generating corresponding procedural materials without requiring manual user intervention. The machine learning model independently performs the translation from visual reference to procedural output, significantly improving efficiency while maintaining quality.

Inventive Principle:

Principle #25Self-service

2Productivity

If random creation techniques are used to generate procedural materials, then generation speed is improved, but fine-tuned user control is lost and creative capabilities are limited

Engineering Contradiction:

Improvegeneration speedVSAvoiduser control and creative capabilities

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system applies local quality by allowing users to provide specific visual references for the exact creative properties they desire. Instead of random generation, the machine learning model analyzes the local visual characteristics of the reference image and reproduces those specific properties in the procedural material, ensuring both speed and creative control.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The machine learning model automatically adjusts procedural parameters based on the visual properties detected in the reference image. This parameter translation enables the system to generate materials with specific creative properties at high speed, avoiding random generation while maintaining fine-tuned control through the visual reference.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If manual authoring is used to create procedural models, then precision and control are maintained, but time consumption increases significantly

Engineering Contradiction:

Improvevisual property accuracyVSAvoidauthoring time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system uses copying by taking a visual reference image as input and automatically generating a procedural model that replicates its appearance. The machine learning model copies the visual properties from the reference image into procedural parameters, achieving high precision visual fidelity without the time investment required for manual authoring.

Inventive Principle:

Principle #26Copying

4Productivity

If automated random generation is used, then computational efficiency is improved, but the ability to replicate specific visual properties is lost

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidvisual property replication accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements feedback by using the reference image as a visual specification that guides the machine learning model's generation process. The model continuously references the input image's visual properties during generation, ensuring accurate replication while maintaining computational efficiency through automated processing.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning model performs preliminary analysis of the reference image to understand its visual properties before generating the procedural model. This preliminary action allows the system to efficiently generate accurate reproductions by pre-processing the visual information from the reference image.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12561949B2Conditional procedural model generation

Publication Date: 2026.02.24 ADOBE INC

AI summary

Conditional procedural model generation techniques are described that enable generation of procedural models that are usable to recreate a visual appearance of an input image. A content processing system, for instance, receives a training dataset that includes a plurality of training pairs. The content processing system trains a machine learning model to generate procedural models based on input images. The content processing system then receives an input image that has a particular visual appearance. The content processing system leverages the trained machine learning model to generate a procedural model that is usable to recreate the particular visual appearance of the input digital image.