Hypernetwork Weights for 3D Face Reconstruction from Single Image
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Solution Overview
Problem
Current methods for 3D human head reconstruction from a single 2D image fail to generate high-quality 3D models due to the inherent lack of information in a single 2D image regarding the 3D geometrical structure and texture of the face.
Innovation Solution
A computer-based system and method using a hypernetwork to generate weights for an implicit representation network (IRN), which produces multi-view face images from a single input image, and then uses these images to reconstruct a 3D face model by training a network to generate a 3D face model from the multi-view images, incorporating loss terms such as adversarial loss, perceptual loss, mesh-regularization, and parameter regularization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single 2D image is used as input for 3D reconstruction, then the input requirement is simplified and processing speed is improved, but the quality and detail of the generated 3D model deteriorates due to inherent information loss
Solution Approach 1:
The patent introduces an implicit representation network as an intermediary component that processes the single 2D image input and generates multi-view images. This intermediary network bridges the gap between limited input information and the requirements for high-quality 3D reconstruction, enabling the system to achieve both fast processing and high model quality by synthesizing additional view information through learned implicit representations
2Device complexity
If prior art methods are used for 3D reconstruction from 2D images, then the device complexity is reduced, but the quality of the reconstructed 3D head model deteriorates
Solution Approach 1:
The patent employs a nested architecture where an implicit representation network is embedded within a hypernetwork framework. The implicit representation network contains embedded attention mechanisms and multi-scale feature extraction components nested within it. This nested structure enables complex high-quality reconstruction functionality while maintaining a relatively simple overall system interface, as the complexity is organized in hierarchical layers rather than requiring multiple separate complex systems
Data Source
AI summary
System and method for reconstructing a three-dimensional (3D) face model from an input 2D image of the face, including: feeding the input 2D image of the first face into a hypernetwork (H) to generate weights for an implicit representation network (IRN); generating, by the IRN with the generated weights, multi-view face images of the first face; and generating the 3D model of the first face by feeding the multi-view face images of the first face into a network trained to generate a 3D face model from the multi-view face images.