View-Dependent 3D Facial Models for Accurate 2D Projection Across Poses
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Solution Overview
Problem
Existing methods for generating 3D facial models are challenging due to the difficulty in maintaining accurate projection of the model onto 2D images across various view angles, often requiring expensive equipment and controlled lighting, and lack flexibility in generating models for diverse poses without individual-specific tailoring.
Innovation Solution
A view-dependent 3D modeling system that uses a machine learning model to condition 3D facial model parameters based on pose, allowing for accurate 2D image generation from a wide range of poses using a trained 3D model engine that includes a 3D model fitting technique and face reconstruction engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing methods are used to generate 3D facial models, then accurate projection onto 2D images can be achieved, but expensive equipment and controlled lighting are required
Solution Approach 1:
The patent replaces complex mechanical/optical systems (multiple cameras, controlled lighting equipment) with a machine learning model that processes standard images to generate 3D facial models. The neural network learns to infer 3D geometry and lighting conditions from 2D images, substituting physical measurement systems with computational inference.
Solution Approach 2:
The patent creates a computational copy of the 3D facial model from 2D image data using machine learning. Instead of requiring physical 3D scanning equipment, the system learns to reconstruct 3D facial geometry by training on paired 2D-3D data, effectively copying 3D information from 2D images through pattern recognition.
2Adaptability or versatility
If existing methods are used to generate 3D facial models, then models can be generated, but flexibility in generating models for diverse poses is limited
Solution Approach 1:
The patent introduces dynamic pose estimation capabilities by training the machine learning model on diverse pose data during training. The model learns to estimate 3D facial geometry for various head orientations and expressions, making the system adaptable to diverse poses while maintaining accuracy through learned pose-invariant features.
Solution Approach 2:
The patent changes the parameter space by incorporating pose parameters (rotation angles, translation) as additional inputs or conditioning variables in the neural network. This allows the model to generate accurate 3D facial models for diverse poses by adjusting its output based on the estimated pose parameters, rather than requiring individual-specific tailoring for each pose.
3Manufacturing precision
If existing methods are used to generate 3D facial models, then individual-specific accuracy can be achieved, but the process is time-consuming and less efficient
Solution Approach 1:
The patent replaces time-consuming manual or iterative 3D scanning and modeling processes with a trained machine learning model that generates 3D facial models in real-time or near-real-time. The neural network has learned individual-specific features during training, enabling accurate reconstruction without requiring lengthy data collection or processing sessions for each new subject.
Data Source
AI summary
Systems and techniques are provided for generating a three-dimensional (3D) facial model. For example, a process can include obtaining at least one input image associated with a face. In some aspects, the process can include obtaining a pose for a 3D facial model associated with the face. In some examples, the process can include generating, by a machine learning model, the 3D facial model associated with the face. In some cases, one or more parameters associated with a shape component of the 3D facial model are conditioned on the pose. In some implementations, the 3D facial model is configured to vary in shape based on the pose for the 3D facial model associated with the face.