Anatomically Constrained Implicit Shape Models for Nonlinear Deformation
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Solution Overview
Problem
Conventional linear 3D morphable models (3DMMs) are unable to represent continuous, nonlinear deformations of shapes, leading to unrealistic motion and physically impossible shapes, and require iterative optimization that increases resource overhead and latency in facial animation and reconstruction.
Innovation Solution
The technique involves determining ground truth positions of points on a target shape, generating fitting parameters using neural networks, and computing predicted positions based on these parameters, while training the neural networks to minimize losses associated with the predicted and ground truth positions, ultimately generating a 3D model that adheres to anatomical constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If linear 3D morphable models are used to represent shapes, then the model is simple and computationally efficient, but it cannot represent continuous nonlinear deformations and produces unrealistic shapes
Solution Approach 1:
The patent transforms the linear parameter space of traditional 3DMM into a nonlinear manifold space using autoencoders and optimization techniques. The shape parameters are no longer simple linear coefficients but optimized latent vectors that capture nonlinear anatomical variations, enabling realistic deformation while maintaining model compactness
Solution Approach 2:
The patent combines multiple anatomical constraints (skull structure, jaw bone, skin patches) into a composite constraint system. This composite model integrates rigid anatomical structures with flexible soft tissue regions, allowing the system to represent complex nonlinear deformations while preserving anatomical plausibility
2Reliability
If iterative optimization is used to fit 3DMM parameters with anatomical constraints, then anatomical plausibility improves, but resource overhead and latency increase significantly
Solution Approach 1:
The patent pre-computes and stores optimal shape parameters and anatomical constraint configurations during an offline training phase. This preliminary action creates lookup tables and pre-fitted models that can be quickly applied during runtime, eliminating the need for iterative optimization during real-time facial animation while preserving anatomical plausibility
Solution Approach 2:
The patent replaces the iterative numerical optimization mechanism with a direct neural network-based parameter prediction system. The autoencoder and trained models directly output fitted parameters from input data, substituting the computational mechanics of iterative solvers with a single-pass differentiable transformation that achieves the same anatomical constraint satisfaction much faster
Data Source
AI summary
One embodiment of the present invention sets forth a technique for fitting a shape model for an object to a set of constraints associated with a target shape. The technique includes determining, based on the set of constraints, one or more ground truth positions of one or more points on the target shape. The technique also includes generating, via execution of a set of neural networks, a set of fitting parameters associated with the point(s) and computing, via the shape model, one or more predicted positions of the point(s) based on the set of fitting parameters. The technique further includes training the set of neural networks based on one or more losses associated with the predicted position(s) and the ground truth position(s) and generating, via execution of the trained set of neural networks, a three-dimensional (3D) model corresponding to the target shape.