Inference Model for 2D-to-3D Image Deformation Control
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Solution Overview
Problem
Existing methods for generating three-dimensional representations from two-dimensional images are cumbersome and require significant effort, especially for designers unfamiliar with the process, as they involve defining and adjusting deformations for various angles to maintain the desired impression of the original two-dimensional image.
Innovation Solution
An inference model construction method that uses machine learning to infer deformations of two-dimensional image parts based on control point distributions, allowing for easy generation of three-dimensional representations by deforming two-dimensional images to achieve desired rendering representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deformation of each part is defined with respect to a two-dimensional image drawn from the reference direction to generate three-dimensional representations in different directions, then the desired rendering representation can be achieved, but the workload and complexity of the process increases significantly
Solution Approach 1:
The system automatically performs the deformation definition process by learning from training data. The deformation definition unit infers deformation parameters for generating three-dimensional representations in different directions without requiring manual definition, allowing the system to serve itself in the complex task of deformation parameter specification.
Solution Approach 2:
The manual mechanical process of defining deformation parameters for each part is replaced by a machine learning system. The deformation definition unit uses learned models to automatically determine deformation parameters, substituting the manual mechanical adjustment process with an automated computational approach.
2Manufacturing precision
If manual definition and adjustment of deformations for various angles is performed to maintain the desired impression of the original two-dimensional image, then the rendering quality is improved, but the time and effort required increases
Solution Approach 1:
The system performs preliminary learning by training the deformation definition unit on training data that includes deformation parameters for multiple directions. This preliminary action prepares the model in advance, so that when actual three-dimensional representations need to be generated, the deformation parameters can be quickly inferred without manual adjustment for each angle.
Solution Approach 2:
The deformation definition unit automatically infers deformation parameters based on the learned model, eliminating the need for manual time-consuming adjustment processes. The system serves itself by automatically determining the appropriate deformation parameters to maintain the desired impression across different viewing angles.
3Ease of operation
If the deformation definition process is simplified for ease of use, then the ease of operation improves, but the ability to generate accurate three-dimensional representations may be compromised
Solution Approach 1:
The complex manual deformation definition process is replaced by an automated machine learning system. Users simply need to provide training data, and the deformation definition unit automatically learns and infers the appropriate deformation parameters, maintaining accuracy without requiring users to understand or manually define complex deformation parameters.
Solution Approach 2:
The system automatically performs the complex task of learning and inferring deformation parameters from training data. This self-service capability allows the system to maintain high rendering accuracy while requiring minimal user input, effectively bridging the gap between ease of operation and manufacturing precision.
Data Source
AI summary
An inference model construction method obtains a distribution of control points pertaining to a reference state and a distribution of the control points pertaining to a defined state with respect to a defined representation model. The method extracts a first feature value based on the distribution of the control points pertaining to the reference state. The method machine-learns the distribution of the control points pertaining to the defined state while using, as a label, the first feature value, and constructs an inference model based on a result of the leaning performed with respect to a plurality of the defined representation models.