Procedural Yarn Model Fitting CT Data for Realistic Fabric Rendering
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Solution Overview
Problem
Conventional fiber-based models capture the rich visual appearance of fabrics but are cumbersome to design and edit, while yarn-based procedural models lack fiber-level details, resulting in unrealistic fabric representations in computer graphics applications.
Innovation Solution
An automated approach that fits computed tomography (CT) data to procedural yarn models to recover model parameters, incorporating fiber-level details and enabling easy editing, thereby bridging the gap between complexity and convenience in fabric modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fiber-based models are used to capture rich visual appearance, then visual realism is improved, but model complexity and difficulty of editing increase
Solution Approach 1:
The patent segments the fabric model into hierarchical levels: yarn-level procedural models and fiber-level procedural models. The yarn model handles macroscopic structure and appearance, while the fiber model handles microscopic details. This segmentation allows each level to be modeled independently with appropriate complexity, avoiding the need for fully detailed fiber models throughout the entire fabric representation.
Solution Approach 2:
The patent implements nesting by placing fiber-level procedural models inside yarn-level procedural models. The fiber models are contained within the yarn structures, allowing fiber details to be generated only where needed within the yarn volume. This nested structure enables realistic fiber-level appearance while maintaining the simplicity of yarn-level procedural modeling for overall fabric structure.
2Ease of operation
If yarn-based procedural models are used for convenience and easy modeling, then ease of operation is improved, but fiber-level details are lost resulting in unrealistic appearance
Solution Approach 1:
The patent introduces dynamic procedural generation of fiber models within yarn structures. Instead of static yarn models, the system dynamically generates fiber-level details on-demand during rendering. The fiber models can be configured with adjustable parameters for density, orientation, and morphology, allowing realistic variation while maintaining procedural control and ease of modeling.
3Reliability
If full fiber-level model realizations are generated, then visual realism is improved, but memory usage and processing time increase significantly
Solution Approach 1:
The patent implements partial realization by generating fiber-level models only for the portions of yarns that are visible or relevant to the current view. Instead of realizing all fibers in the entire fabric, the system selectively generates fiber details where they contribute to visual realism, leaving other areas in procedural form. This partial action significantly reduces memory usage while maintaining visual quality in critical areas.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach generates high-quality, realistic fabric models with fiber-level details, improving the virtual realism of rendered cloth appearance and facilitating richly detailed fabric designs with efficient memory usage and performance.
Implementation Method 1
fitting procedural yarn model parameters to input data comprising computed tomography measurements of one or more actual yarn samples
Data Source
AI summary
An apparatus in one embodiment comprises a multi-stage processing pipeline configured to generate a procedural yarn model by fitting procedural yarn model parameters to input data comprising computed tomography measurements of one or more actual yarn samples. The apparatus further comprises an image rendering system configured to execute one or more procedural yarn generation algorithms utilizing the procedural yarn model and to generate based at least in part on results of execution of the one or more procedural yarn generation algorithms at least one corresponding output image for presentation on a display. The multi-stage processing pipeline comprises a first stage configured to perform ply twisting estimation and ply cross-sectional estimation, a second stage configured to classify constituent fibers into regular fibers and flyaway fibers, and third and fourth stages configured to process the respective regular and flyaway fibers to fit respective different sets of parameters of the procedural yarn model.