3D Head Model Reconstruction from Single Image via Neural Parameter Regression
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Solution Overview
Problem
Current methods for 3D human head reconstruction from a single image often fail to generate high-quality models due to the inherent lack of 3D geometrical and textual information in a 2D image, resulting in incomplete or low-resolution 3D head models with limited animation capabilities.
Innovation Solution
A computer-based system and method that involves feeding a 2D image into preparatory networks to generate image features, which are then used to predict parameters of a 3D face model, camera parameters, hair, and wrinkle parameters, followed by a UV texture map generation and completion process to create a fully rigged and animated 3D head model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single 2D image is used for 3D head reconstruction, then the input process is simple and fast, but the generated 3D model lacks high quality and completeness due to insufficient 3D geometrical and texture information
Solution Approach 1:
The patent introduces an intermediary 3D parametric face model as a mediator between the 2D input image and the final 3D head model. This parametric model serves as a bridge that transforms limited 2D information into a structured 3D representation, enabling high-quality reconstruction while maintaining fast processing speeds through parameter regression rather than complex iterative optimization
Solution Approach 2:
The patent transforms the 3D head reconstruction problem into a parameter regression task by representing the face model through a set of parameters (shape, expression, pose, lighting). By changing from direct geometry reconstruction to parameter estimation, the system achieves both speed (through efficient neural network regression) and quality (through comprehensive parameter control over 3D geometry and appearance)
2Manufacturing precision
If multiple images or complex capture setups are used, then high-quality 3D models can be generated, but the system complexity and ease of operation deteriorate
Solution Approach 1:
The patent extracts and focuses on the essential information needed for 3D reconstruction by taking only a single 2D image as input, removing the complexity of multiple images or specialized capture equipment. The system extracts key features (landmarks, textures, depth cues) from this minimal input and uses them to drive the parametric model, achieving high quality results without operational complexity
Solution Approach 2:
The patent transitions from 2D image space to 3D model space through a learned mapping in parameter space. By introducing the parametric model as an intermediate representation, the system effectively adds dimensional transformation capabilities without requiring multiple 2D images, converting a single 2D input into a complete 3D output through parameter-based synthesis
3Productivity
If prior art methods are used for 3D reconstruction, then some 3D models can be generated, but they fail to achieve high quality due to incomplete parameter regression and lack of UV texture completion
Solution Approach 1:
The patent performs preliminary action by first regressing all necessary parameters (shape, expression, pose, lighting) and generating an initial UV texture map before final 3D model construction. This preliminary parameter estimation and texture generation prepare the foundation for high-quality reconstruction, ensuring that when the final model is built, all geometric and appearance parameters are already optimized
Solution Approach 2:
The patent replaces traditional mechanical or iterative optimization systems with a neural network-based parameter regression system. Instead of using complex iterative algorithms to estimate 3D parameters from 2D images, the system uses a trained neural network to directly regress parameters, achieving both speed and accuracy. This substitution also includes replacing manual UV mapping processes with automated UV texture completion networks
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 into at least one preparatory network to generate 3D landmarks (L′), facial features (F′) and segmentation maps (S′); feeding the 3D landmarks (L′), the facial features (F′), and the segmentation maps (S′) to a parameter regressor network (M1) to predict parameters of a 3D face parametric model (P′), camera parameters (C′), hair parameters (H′) and wrinkle parameters (W′); generating an initial UV texture map (UV_0′) using the input 2D image and (P′, C′, H′, W′); feeding the initial UV texture map (UV_0′) into a UV completion network (M2) to generate a full UV texture map (UV′) and illumination parameters (LI′); and generating the 3D face model using (P′, C′, H′, W′, UV′, LI′).