Incremental 2D-to-3D Pose Lifting With Projection Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D human pose estimation methods from 2D images are inaccurate and computationally inefficient, lacking a feedback mechanism to optimize geometric projection ambiguities.
Innovation Solution
An incremental 2D-to-3D pose lifting method using an iterative process with residual regression models to refine 3D pose estimates, projecting 3D poses back to 2D space for feedback and adjusting with residual differences, employing fully connected or graph convolutional networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lifting network is trained to directly estimate 3D human pose from 2D body joint locations, then 3D pose estimation accuracy is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent segments the 3D pose estimation process into two stages: a fast coarse estimation stage using a lightweight lifting network, and a refinement stage using iterative optimization with residual regression models. This segmentation allows the system to achieve high accuracy through the refinement stage while maintaining computational efficiency in the coarse estimation stage, resolving the contradiction between accuracy and computational efficiency.
Solution Approach 2:
The patent applies preliminary action by first obtaining a coarse 3D pose estimate using a lifting network before performing iterative refinement. This preliminary coarse estimate serves as an initial guess that guides the subsequent iterative optimization process, reducing the computational burden and enabling faster convergence to accurate 3D pose estimates.
2Measurement precision
If iterative refinement with projection feedback is implemented, then 3D pose accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements feedback by projecting the estimated 3D pose back to 2D space and comparing it with the original 2D pose measurements. The difference (residual) is then used to generate correction terms that refine the 3D pose estimate in subsequent iterations. This feedback mechanism systematically reduces projection errors and improves accuracy while controlling processing time through efficient iterative optimization.
Solution Approach 2:
The patent applies parameter changes by using different residual regression models for different refinement stages, where each model is optimized for specific error patterns at that stage. The models dynamically adjust their parameters based on the current iteration and the magnitude of corrections needed, allowing efficient convergence without excessive processing time.
3Measurement precision
If geometric projection ambiguities are addressed through iterative correction, then pose estimation accuracy is improved, but model complexity increases
Solution Approach 1:
The feedback mechanism projects 3D poses back to 2D space and uses the residual differences to guide iterative corrections. This systematic feedback approach handles geometric projection ambiguities by continuously refining the estimate based on observed 2D measurements, improving accuracy without requiring overly complex models.
Solution Approach 2:
The patent uses parameter changes through stage-specific residual regression models that adapt to different refinement needs. Each model is trained to handle specific error patterns at its stage, providing targeted corrections for geometric ambiguities without requiring a single overly complex monolithic model.
Data Source
AI summary
Techniques related to 3D pose estimation from a 2D input image are discussed. Such techniques include incrementally adjusting an initial 3D pose generated by applying a lifting network to a detected 2D pose in the 2D input image by projecting each current 3D pose estimate to a 2D pose projection, applying a residual regressor to features based on the 2D pose projection and the detected 2D pose, and combining a 3D pose increment from the residual regressor to the current 3D pose estimate.