3D Mesh Deformation via Deep Learning Vertex Offsets
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Solution Overview
Problem
Existing 3D mesh modification techniques are difficult, time-consuming, and expensive, requiring extensive user interaction and large training datasets, with unsatisfactory performance and the need for minimal user input and supervision to deform a 3D source mesh to resemble a target object or image.
Innovation Solution
An end-to-end Deep Neural Network architecture that predicts per-vertex offsets to morph a source 3D mesh into a target representation using unsupervised training with loss functions for distance minimization, smoothing, self-intersection avoidance, and detail preservation, allowing for efficient creation of a hybrid 3D object with minimal user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional 3D mesh modification techniques are used, then artistic capability and expertise are required to achieve desired modifications, but the process becomes difficult, time-consuming, and expensive
Solution Approach 1:
The patent replaces manual artistic manipulation with an automated deep learning system. The neural network automatically learns correspondence between source and target meshes through unsupervised training, eliminating the need for artists to manually annotate vertex correspondences or use specialized tools, thereby reducing both time and cost while maintaining modification quality
Solution Approach 2:
The system performs self-training through unsupervised learning where the neural network automatically discovers and learns vertex correspondences between meshes without human intervention. The loss functions guide the network to self-correct and optimize its deformation predictions, making the system self-sufficient and eliminating dependency on expert users
2Reliability
If computer-based systems with annotated vertex correspondences are used, then user input is required, but the system requires large quantities of training data which is inconvenient and impractical
Solution Approach 1:
The neural network performs unsupervised learning by automatically learning vertex correspondences from the geometric structure of source and target meshes themselves. The system uses loss functions that measure deformation quality and self-correct without requiring external annotated training data, making the system self-sufficient and eliminating the need for large training datasets
Solution Approach 2:
The patent introduces geometric constraints and loss functions as intermediaries that mediate between the neural network's deformation predictions and the target mesh. These loss functions (L1/L2 norms, mesh Laplacian, volume preservation) act as guides that enable the network to learn effective correspondences without requiring annotated training data, bridging the gap between input meshes and desired output
3Productivity
If existing 3D mesh databases are searched, then existing models can be found quickly, but typically a designer will not be able to find a 3D mesh that exactly matches their needs
Solution Approach 1:
The patent segments the mesh modification task into learnable components: the neural network learns to predict per-vertex offsets independently for each vertex of the source mesh. This segmentation allows the system to process and transform any source mesh efficiently while achieving precise matching to the target, combining the speed of database search with the accuracy of custom modeling
Data Source
AI summary
Techniques are disclosed for deforming a 3D source mesh to resemble a target object representation which may be a 2D image or another 3D mesh. A methodology implementing the techniques according to an embodiment includes extracting a set of one or more source features from a source 3D mesh. The source 3D mesh includes a plurality of source points representing a source object, and the extracting of the set of source features is independent of an ordering of the source points. The method also includes extracting a set of one or more target features from the target object representation, and decoding a concatenation of the set of source features and the set of target features to predict vertex offsets for application to the source 3D mesh to generate a deformed 3D mesh based on the target object. The feature extractions and the vertex offset predictions may employ Deep Neural Networks.