Neural Network Architecture for 3D Face Alignment
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Solution Overview
Problem
Current 3D face alignment methods using deep neural networks face challenges in reducing computational and memory costs, making them unsuitable for mobile devices due to high time and space complexities.
Innovation Solution
A neural network architecture utilizing depthwise separable blocks (DSBs) with a combination of depthwise and pointwise convolutional layers, reducing network parameters from megabytes to kilobytes, enabling efficient 3D face alignment on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep neural networks are used for 3D face alignment, then measurement precision is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent divides the face alignment task into two separate stages: first detecting 2D landmark coordinates, then estimating depth coordinates. This segmentation allows each module to be optimized independently, with the depth estimation module being much lighter than traditional end-to-end 3D alignment networks, thus reducing overall device complexity while maintaining precision.
Solution Approach 2:
The patent introduces an intermediate representation (2D landmarks with associated feature vectors) between image input and final 3D output. This intermediary structure enables the system to process information in a more efficient manner, reducing the computational burden compared to direct 3D landmark regression while preserving measurement accuracy.
2Measurement precision
If traditional deep neural networks are used for 3D face alignment, then measurement precision is improved, but processing speed decreases
Solution Approach 1:
By separating 2D detection and 3D estimation into distinct modules, the patent enables parallel processing and optimization of each stage. The lightweight depth estimation module processes information much faster than traditional heavy networks, significantly improving processing speed while maintaining overall accuracy through the coordinated work of both modules.
Solution Approach 2:
The patent extracts the depth estimation task from the traditional monolithic deep network and handles it separately using a lightweight module. This extraction removes the computational bottleneck from the main processing pipeline, allowing the system to achieve high processing speeds while preserving measurement precision through the extracted depth information.
3Measurement precision
If traditional deep neural networks are used for 3D face alignment, then measurement precision is improved, but memory cost increases
Solution Approach 1:
The patent segments the model parameters into two distinct sets: one for 2D landmark detection and another for depth estimation. The depth estimation module uses significantly fewer parameters than traditional 3D alignment networks. This segmentation allows the system to achieve high measurement precision with much lower total memory requirements, as each segment is optimized for its specific task rather than requiring a large universal model.
Data Source
AI summary
A system, neural network, and corresponding method generate 3D landmarks associated with an object in a 2D image. An embodiment is a system comprising a neural network detector configured to produce planar coordinates of landmarks at points of the object in the 2D image and a depth coordinate estimator. The planar coordinates include planar coordinate pairs. The depth coordinate estimator is configured to receive the 2D image and the planar coordinates and to estimate a depth coordinate for each planar coordinate pair of each landmark to generate the 3D landmarks. The system reduces network parameters from MB to KB and has better performance relative to state-of-the-art methods. The system may be configured to apply the 3D landmarks for face alignment, virtual face makeup, face recognition, eye gaze tracking, face synthesis, or other face related application.