Anatomically Constrained Implicit Shape Models for Nonlinear Deformations

The patent transforms the linear combination parameters into nonlinear parameters by introducing anatomical constraint parameters (skull, jaw bone, skin patches) that govern deformation in a nonlinear manner. This allows the model to represent continuous nonlinear deformations while maintaining a structured parameter space for efficient computation.

The patent combines multiple anatomical structures (skull, jaw bone, skin patches) into a composite hierarchical model where each component has specific deformation properties. This composite structure enables realistic nonlinear facial deformations by integrating the mechanical behaviors of different tissue types.

The patent pre-computes anatomical constraints (skull, jaw bone, skin patches) from training data and stores them as prior knowledge. During runtime, these pre-computed constraints are directly applied to guide deformation, eliminating the need for iterative optimization and achieving real-time processing speeds while ensuring anatomical plausibility.

The patent replaces the iterative mechanical optimization system with a direct neural network-based computation system. The neural networks predict anatomically plausible deformations by learning from training data, substituting the slow iterative optimization process with fast parallel computation.

The patent substitutes the iterative mechanical optimization process with a neural network-based direct computation approach. The neural networks are trained to directly predict anatomically constrained deformations, eliminating the need for iterative optimization while maintaining constraint satisfaction.

The patent performs preliminary training of neural networks on anatomically constrained data during the offline phase. This pre-learning enables the model to directly generate anatomically plausible results during runtime without requiring iterative optimization, significantly reducing computational time.