3D Pose Estimation Using Unlabeled Multi-View Data
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Solution Overview
Problem
Current methods for estimating 3D human body pose from 2D images are limited by the lack of diverse training data and require manual annotations, which are difficult to obtain and prone to errors, especially in unconstrained outdoor environments.
Innovation Solution
A method for training a 3D pose estimation model using unlabeled multi-view data, where a neural network predicts 2.5D pose representation and performs scale normalization to estimate 3D pose from 2D images without requiring 3D annotations, leveraging multi-view consistency and limb length losses for robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotations are used for training, then measurement precision of 3D pose is improved, but loss of time and ease of manufacture deteriorate due to difficulty in obtaining annotations
Solution Approach 1:
The system uses automatically generated 3D pose labels from multi-view geometry and 2D pose detections instead of manual annotations. The unsupervised learning framework enables the model to learn 3D pose estimation from unlabeled multi-view image data, eliminating the need for time-consuming manual 3D pose annotation while maintaining estimation accuracy.
Solution Approach 2:
The patent generates pseudo-ground truth 3D pose labels by copying and transforming 2D pose information across multiple views using camera calibration matrices and projection geometry. This creates synthetic training labels that replicate the information content of manual annotations without requiring human labor.
2Measurement precision
If controlled indoor datasets are used for training, then measurement precision is improved, but adaptability to unconstrained outdoor environments deteriorates
Solution Approach 1:
The unsupervised learning framework using unlabeled multi-view data serves multiple functions: it works for both controlled indoor and unconstrained outdoor environments, adapts to different camera configurations, and handles various pose conditions. This universal approach eliminates the need for environment-specific training datasets.
Solution Approach 2:
The model learns to adapt to different environmental conditions by processing multi-view images with varying parameters such as camera positions, lighting conditions, and background scenarios. The unsupervised framework enables the model to adjust to different data distributions without requiring re-annotation for each environment.
3Ease of manufacture
If 2D annotations are used for weak supervision, then ease of manufacture is improved, but measurement precision deteriorates due to insufficient 3D pose information
Solution Approach 1:
The patent transitions from 2D pose annotations to 3D pose estimation by utilizing the additional dimension provided by multi-view geometry. By processing images from multiple camera angles and applying 3D projection models, the system recovers depth information and 3D joint positions that cannot be obtained from single-view 2D annotations alone.
Solution Approach 2:
The system uses 2D pose detections as an intermediary step to generate initial 3D pose hypotheses, which are then refined using multi-view consistency constraints and 3D body model priors. This intermediary approach leverages the ease of obtaining 2D annotations while achieving accurate 3D pose estimation through the additional geometric constraints.
Data Source
AI summary
Learning to estimate a 3D body pose, and likewise the pose of any type of object, from a single 2D image is of great interest for many practical graphics applications and generally relies on neural networks that have been trained with sample data which annotates (labels) each sample 2D image with a known 3D pose. Requiring this labeled training data however has various drawbacks, including for example that traditionally used training data sets lack diversity and therefore limit the extent to which neural networks are able to estimate 3D pose. Expanding these training data sets is also difficult since it requires manually provided annotations for 2D images, which is time consuming and prone to errors. The present disclosure overcomes these and other limitations of existing techniques by providing a model that is trained from unlabeled multi-view data for use in 3D pose estimation.