Multi-dimensional Model Shape Transfer via ML Style Application
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Solution Overview
Problem
Current design processes are inefficient and resource-intensive due to their iterative nature, lacking tools to reduce the number of iterations and increase efficiency in capturing appealing product designs, particularly in applying shapes and styles to product models.
Innovation Solution
A computer-implemented intelligent design platform utilizing multi-dimensional model style transfer through machine learning models, such as PointNet++, to iteratively apply and optimize the shape of a style object to a content object, denoising the stylized object model to enhance design efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional iterative design processes are used to capture appealing product designs, then design quality can be improved through multiple iterations, but resource consumption (processors and memory) and time increase significantly
Solution Approach 1:
The patent uses style transfer technology to copy the shape characteristics of style objects and apply them to content objects. Instead of manually iterating through multiple design versions, the system automatically transfers geometric features, surface properties, and stylistic elements from source objects to target objects, generating multiple design variations efficiently while maintaining high design quality.
Solution Approach 2:
The patent replaces the manual mechanical iterative design process with an automated computational system. Machine learning models and algorithms substitute for human designers' repeated manual adjustments, automatically optimizing design parameters and generating stylized objects without requiring multiple manual iteration cycles, thus improving productivity while maintaining design quality.
2Manufacturing precision
If multiple design iterations are performed to refine product shapes and styles, then design appeal can be enhanced, but the number of iterations and resource demand increase
Solution Approach 1:
The patent performs preliminary action by pre-processing style objects to extract and encode their shape characteristics, geometric features, and stylistic properties into reusable templates or feature representations. This preliminary extraction allows the system to rapidly apply these pre-processed style features to multiple content objects without requiring full iterative refinement for each design, significantly reducing design iteration time while maintaining design appeal.
Solution Approach 2:
The patent utilizes parameter changes by adjusting key geometric and stylistic parameters through style transfer. The system modifies parameters such as surface curvature, edge sharpness, proportion ratios, and stylistic attributes by transferring them from style objects to content objects. This parameter-based approach enables rapid design refinement without requiring multiple complete iteration cycles, reducing time loss while enhancing design appeal.
3Ease of operation
If traditional design tools are used to manually adjust and refine product models, then design control can be maintained, but resource consumption and process complexity increase
Solution Approach 1:
The patent introduces an intermediary style transfer system that acts as a mediator between the designer's intent and the final design output. The system includes intermediate components such as feature extraction modules, style encoding networks, and parameter optimization algorithms that automatically handle complex transformations. This intermediary layer maintains design control by allowing designers to specify high-level style preferences while the automated system manages the complex computational processes, reducing perceived system complexity for the user.
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 a set of base style feature representations, executing iterations including: generating, by the ML model, a set 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 set 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 including determining that the total loss is optimized, and in response providing the initial stylized object model as output of the iterative process.