Geometry-Guided 3D Head Synthesis for Consistent Expression and Pose
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methodologies in facial image synthesis and animation fail to maintain 3D consistency when synthesizing faces with changing expressions and postures, as they operate on 2D convolutional networks without enforcing underlying 3D facial structure.
Innovation Solution
A geometry-guided 3D GAN framework that conditions 3D representations of head geometry in a canonical space using feature vectors and camera viewing parameters, employing a signed distance function (SDF) for point-to-point volumetric correspondences, and combines these with feature layers to synthesize high-quality 3D objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If 2D convolutional networks are used for facial image synthesis and animation, then the processing complexity is reduced and ease of operation is improved, but 3D consistency is lost when synthesizing faces with changing expressions and postures
Solution Approach 1:
The patent transitions from 2D convolutional networks to 3D convolutional networks, adding the depth dimension to process volumetric data. This dimensional upgrade enables the network to capture and enforce 3D facial structure constraints while maintaining expression and pose variations, thereby preserving 3D consistency without sacrificing operational feasibility
Solution Approach 2:
The patent introduces a canonical space as an intermediary representation that encodes 3D facial geometry. This canonical space acts as a mediator between the input 2D images and the output synthesized images, providing a structured 3D framework that ensures geometric consistency across different expressions and poses while allowing flexible manipulation
2Manufacturing precision
If 3D representations and volumetric correspondences are used to maintain 3D consistency, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex 3D synthesis task into distinct components: (1) extracting volumetric correspondences between input images and a canonical 3D model, (2) warping the canonical model using these correspondences, and (3) synthesizing the final image from the warped 3D representation. This segmentation reduces overall system complexity by breaking down the monolithic 3D processing into manageable modular steps
Solution Approach 2:
The patent performs preliminary action by pre-establishing the canonical 3D facial model with known geometric constraints before processing the synthesis task. This pre-computed canonical space serves as a ready-made template that enforces 3D consistency, eliminating the need to learn 3D geometry from scratch during the synthesis process and thereby reducing computational complexity
Data Source
AI summary
Technologies are described and recited herein for producing controllable synthesized images include a geometry guided 3D GAN framework for high-quality 3D head synthesis with full control on camera poses, facial expressions, head shape, articulated neck and jaw poses; and a semantic SDF (signed distance function) formulation that defines volumetric correspondence from observation space to canonical space, allowing full disentanglement of control parameters in 3D GAN training.