3D Human Pose Estimation via Multi-View Neural Constraints
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Solution Overview
Problem
Existing three-dimensional human pose estimation methods face challenges in achieving accurate and stable results, particularly when dealing with multi-frame and multi-view video frames, as they often fall into local extrema due to lack of robust constraints.
Innovation Solution
The method employs a neural network model to extract initialization pose estimation results, which are used as two-dimensional clue constraints for multi-frame and multi-view human pose estimation, incorporating single-frame and multi-view constraints to prevent local extrema and enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a neural network model is used to extract initialization pose estimation results, then the estimation speed is improved, but the accuracy deteriorates due to falling into local extrema
Solution Approach 1:
The patent introduces an intermediary optimization module that acts as a bridge between the neural network's initialization output and the final pose estimation. This intermediary step uses optimization algorithms to refine the initial estimates, correcting errors and avoiding local extrema while preserving the speed advantage of neural network initialization.
Solution Approach 2:
The patent divides the pose estimation process into two distinct segments: a fast initialization phase using neural networks and a refinement phase using optimization algorithms. This segmentation allows each component to specialize - the neural network provides rapid initial estimates while the optimization module ensures accuracy, resolving the contradiction between speed and precision.
2Device complexity
If single-frame and single-view estimation is performed, then the computational complexity is reduced, but the reliability deteriorates due to lack of constraints
Solution Approach 1:
The patent implements a dynamic multi-view constraint mechanism that adaptively applies constraints across multiple views and frames. The system dynamically adjusts the level of constraint application based on the specific estimation context, maintaining reliability while avoiding excessive computational complexity through intelligent resource allocation.
Solution Approach 2:
The patent creates a universal estimation framework that processes multiple views and frames simultaneously using a unified model structure. This multi-functional approach allows the same computational model to handle various viewing angles and temporal frames, improving reliability through consistent constraints without proportionally increasing complexity.
3Measurement precision
If multi-frame and multi-view constraints are applied, then the pose estimation accuracy is improved, but the loss of time increases due to processing more data
Solution Approach 1:
The patent applies preliminary action by using neural networks to generate high-quality initialization estimates before the optimization process begins. This preliminary step provides the optimization algorithm with a much better starting point, allowing it to converge faster when processing multi-frame and multi-view data, thus reducing the time penalty associated with increased constraints.
Solution Approach 2:
The patent dynamically adjusts optimization parameters such as convergence thresholds and iteration limits based on the specific multi-view and multi-frame data being processed. By adapting these parameters to the actual data complexity, the system maintains high accuracy while minimizing unnecessary processing time for simpler cases.
Data Source
AI summary
This application discloses a three-dimensional human pose estimation method performed by a computer device. An initialization pose estimation result of a single video frame in a video frame sequence of n views is extracted based on a neural network model. Single-frame and single-view human pose estimation is performed on the initialization pose estimation result for each video frame, to obtain n single-view pose estimation sequences respectively corresponding to the n views. Single-frame and multi-view human pose estimation is performed according to single-view pose estimation results with the same timestamp in the n single-view pose estimation sequences, to obtain a multi-view pose estimation sequence. Multi-frame and multi-view human pose estimation is performed on a multi-view pose estimation result in the multi-view pose estimation sequence, to obtain a multi-view and multi-frame pose estimation result. Therefore, accuracy of human pose estimation is improved.