Multi-dimensional Model Texture Transfer for 3D Design Efficiency
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Solution Overview
Problem
Current design processes are resource-intensive and iterative, lacking tools to reduce the number of iterations and increase efficiency, particularly in applying textures and styles to product designs.
Innovation Solution
A computer-implemented intelligent design platform using multi-dimensional model style transfer, employing machine learning models like MeshNet and PointNet for iterative texture application, determining optimized stylized feature representations, and smoothing to generate a stylized object model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional iterative design processes are used to apply textures and styles to product designs, then design flexibility and creativity are maintained, but resource consumption increases and design efficiency decreases
Solution Approach 1:
The patent replaces traditional manual iterative design processes with an automated machine learning-based style transfer system. The system uses neural networks to automatically apply styles and textures to 3D models, eliminating the need for repeated manual rendering and adjustment cycles, thereby significantly reducing computational resource consumption while maintaining design flexibility
Solution Approach 2:
The system creates stylized versions of 3D models by copying and transforming existing model data through machine learning algorithms. Instead of repeatedly generating entirely new designs through manual iteration, the system copies the structural information from source models and applies learned style transformations, reducing the computational burden of creating multiple variant designs
2Manufacturing precision
If multiple design iterations are performed to capture appealing designs, then design quality improves, but the time required for design processes increases
Solution Approach 1:
The system performs preliminary style extraction and feature learning during an initial training phase using a comprehensive dataset of styled and unstyled models. This pre-computed style information is then readily available for rapid application to new designs, eliminating the need to relearn styles during each design iteration and significantly reducing the time required to generate multiple design variants
Solution Approach 2:
The system incorporates feedback mechanisms where the generated stylized designs can be evaluated and used to refine the style transfer parameters. This allows the system to automatically adjust and optimize style applications based on design quality metrics, reducing the number of manual iterations needed to achieve acceptable design quality
Data Source
AI summary
Implementations are directed to processing a content object model through a ML model to provide a set of base content feature representations, processing a style object model through the ML model to provide sets of base style feature representations, executing iterations including: generating, by the ML model, sets of stylized feature representations for an initial stylized object model, the initial stylized object model having one or more adjusted parameters relative to a previous iteration, determining a total loss based on the sets of stylized feature representations, the set of base content feature representations, and the sets of base style feature representations, and determining that the total loss is non-optimized, and in response, initiating a next iteration, executing an iteration of the iterative process, the iteration including determining that the total loss is optimized, and in response providing the initial stylized object model as output of the iterative process.