3D Face Reconstruction from Single Image via Neural Network
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Solution Overview
Problem
Current three-dimensional face reconstruction methods require multiple images or videos for accurate reconstruction and fail to produce lifelike results with a single two-dimensional image, limiting the ability to view the face from various angles and being computationally inefficient.
Innovation Solution
A method that uses a neural network to convert two-dimensional feature points into three-dimensional coordinates, fine-tunes the shape, and compensates color to reconstruct a rotatable three-dimensional face model from a single two-dimensional image, utilizing multi-stage computing from low to high resolution and linear combinations of feature templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple images or videos are used for three-dimensional face reconstruction, then the accuracy and lifelike quality of the reconstruction is improved, but the computational time and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks on large datasets of facial images and storing pre-computed three-dimensional face models in a database. When a new face needs reconstruction, the system quickly matches the input image against stored models rather than performing full reconstruction from scratch, dramatically reducing computational time while maintaining accuracy
Solution Approach 2:
The patent uses copying by creating and storing multiple pre-computed three-dimensional face models in a database that can be quickly retrieved and applied. Instead of reconstructing each face model independently in real-time, the system copies pre-existing models that match the input characteristics, significantly reducing the computational burden
2Loss of time
If only a single two-dimensional image is used for three-dimensional face reconstruction, then the computational time is reduced, but the reconstruction accuracy and ability to produce lifelike results deteriorates
Solution Approach 1:
The patent introduces an intermediary neural network that acts as a bridge between the single two-dimensional input image and the three-dimensional face model. The neural network automatically extracts key facial features and infers three-dimensional structure from the 2D image, enabling accurate reconstruction without requiring multiple input images or complex manual processing
Solution Approach 2:
The patent applies parameter changes by using neural networks to automatically detect and extract facial feature parameters (such as distances between eyes, nose position, mouth shape) from the two-dimensional image. These extracted parameters are then used to configure and generate the corresponding three-dimensional face model, transforming 2D image data into accurate 3D structural parameters
3Shape
If the whole human face is fitted first and then particular areas are fitted, then the overall structure is established, but the computational time increases and accurate fitting results are difficult to achieve
Solution Approach 1:
The patent applies segmentation by dividing the face into multiple key regions (eyes, nose, mouth, cheeks) and processing each region independently through the neural network. This allows parallel processing of different facial features and enables focused optimization of each area's three-dimensional structure without being constrained by a sequential whole-face approach
Data Source
AI summary
The invention is related to a method of three-dimensional face reconstruction by inputting a single face image to reconstruct a three-dimensional face model, therefore, the human face image is seen at various angles of three-dimensional face through rotating the model images.