3D Human Pose Estimation via Weakly Supervised 2D Re-projection
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Solution Overview
Problem
Current methods for 3D human pose estimation from 2D images face challenges in generalizing well to 'in-the-wild' datasets due to reliance on accurate 2D pose estimation, which can be inaccurate, and lack of 3D ground truth, leading to poor performance in diverse and occluded poses.
Innovation Solution
A processor-implemented method for weakly supervised learning of 3D human poses from 2D poses using a neural network that predicts 3D poses and re-projects them back to 2D, minimizing re-projection loss and incorporating geometric constraints to ensure accuracy, allowing training on datasets without 3D ground truth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If weakly supervised learning is used to train on 2D pose datasets, then generalization to in-the-wild data is improved, but accuracy of 2D pose estimation deteriorates due to end-to-end training dependencies
Solution Approach 1:
The patent segments the pose estimation pipeline into two independent stages: (1) a 2D pose detection module that processes images and outputs 2D poses, and (2) a 3D pose estimation module that takes 2D poses as input and outputs 3D poses. This segmentation allows each module to be optimized independently, with the 2D pose detector trained on 2D datasets and the 3D estimator trained on 3D datasets, resolving the dependency problem in end-to-end training.
Solution Approach 2:
The patent introduces 2D poses as an intermediary representation that bridges image data and 3D pose data. Instead of directly estimating 3D poses from images (which requires complex end-to-end training), the system first extracts 2D poses from images and then converts them to 3D poses. This intermediary 2D pose layer simplifies the training process and improves generalization to in-the-wild data.
2Measurement precision
If full 3D supervision is used on controlled datasets, then accuracy on benchmark datasets is improved, but generalization to diverse poses deteriorates
Solution Approach 1:
The patent creates a universal 3D pose estimation system that can handle multiple types of input data (controlled 3D datasets and in-the-wild 2D datasets) through a single unified architecture. The system uses 2D pose estimates from diverse in-the-wild images as input to the 3D estimation module, enabling it to generalize to diverse poses and uncontrolled environments while maintaining accuracy on standard benchmarks.
Data Source
AI summary
Estimating 3D human pose from monocular images is a challenging problem due to the variety and complexity of human poses and the inherent ambiguity in recovering depth from single view. Recent deep learning based methods show promising results by using supervised learning on 3D pose annotated datasets. However, the lack of large-scale 3D annotated training data makes the 3D pose estimation difficult in-the-wild. Embodiments of the present disclosure provide a method which can effectively predict 3D human poses from only 2D pose in a weakly-supervised manner by using both ground-truth 3D pose and ground-truth 2D pose based on re-projection error minimization as a constraint to predict the 3D joint locations. The method may further utilize additional geometric constraints on reconstructed body parts to regularize the pose in 3D along with minimizing re-projection error to improvise on estimating an accurate 3D pose.