3D Human Pose Estimation via Epipolar Geometry Self-Supervision
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Solution Overview
Problem
Current human pose estimation systems face challenges in estimating three-dimensional poses from a single image without requiring 3D supervision or camera extrinsics, and they often rely on limited or costly 3D datasets, necessitating the development of methods that can utilize 2D ground-truth poses and epipolar geometry for self-supervision.
Innovation Solution
A system that uses a data processor with multiple cameras to capture images, extract features, and employ a neural network architecture with a three-dimensional reconstruction module and reprojection module to generate and refine 3D pose estimates, leveraging epipolar geometry for self-supervision and eliminating the need for 3D ground-truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D pose estimation systems use traditional methods with 3D ground-truth data, then measurement precision is improved, but loss of substance increases due to costly and limited 3D datasets
Solution Approach 1:
The system performs self-supervision by automatically generating 3D pose annotations from 2D pose data and epipolar geometry without requiring external 3D ground-truth datasets. The model generates its own training labels through geometric constraints and back-projection, making the system self-sufficient and eliminating dependence on costly 3D annotated data
Solution Approach 2:
The system introduces 2D pose estimates and epipolar geometry as intermediary elements to bridge the gap between available 2D data and required 3D pose information. These intermediaries enable the transformation of 2D observations into 3D pose annotations without direct 3D supervision
2Loss of substance
If the system uses self-supervision with 2D ground-truth poses, then loss of substance is reduced by eliminating 3D annotation requirements, but measurement precision may deteriorate without 3D supervision
Solution Approach 1:
The system implements a feedback mechanism where 3D pose estimates are back-projected to 2D image planes and compared with original 2D pose annotations. This feedback loop provides supervision signals that guide the model to learn accurate 3D pose representations while maintaining consistency with ground-truth 2D observations
Solution Approach 2:
The system transforms the problem from 2D to 3D space by lifting 2D pose estimates into three-dimensional pose using epipolar geometry constraints. This dimensional transformation enables the model to reason about 3D spatial relationships while being supervised by 2D annotations
3Measurement precision
If multiple cameras are used for multi-view 3D pose estimation, then measurement precision is improved through geometric constraints, but device complexity increases
Solution Approach 1:
The system processes images from multiple cameras through a unified neural network architecture that handles multi-view inputs generically. The same network parameters and processing pipeline are used regardless of the number of cameras, making the system adaptable to different camera configurations without requiring separate processing chains for each view
Data Source
AI summary
A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and a second branch pose estimation neural network; (iii) generating, by using a three dimensional reconstruction module, three dimensional reconstructions from two dimensional pose estimates produced by the second branch pose estimation neural network; and (iv) projecting, by using a reprojection module, the three dimensional reconstructions to camera image planes of respective image samples, and uploading the reprojections and image samples to an annotation server.