Multi-Person 3D Pose Tracking via 4D CNN
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Solution Overview
Problem
Current methods for 3D pose estimation from 2D image data are prone to errors, especially in multi-person scenarios with occlusions and varying camera views, as they treat 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking as independent sub-problems, leading to sub-optimal results.
Innovation Solution
A spatio-temporal formulation using a 4D convolutional neural network that operates in a common voxelized feature space, allowing end-to-end learning for simultaneous 3D body joint reconstruction and tracking of multiple individuals, with attention aggregation and differentiable representation matching to handle occlusions and varying camera views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 3D pose estimation is performed by treating 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking as independent sub-problems, then each sub-problem can be solved separately, but the overall tracking accuracy and joint localization deteriorate due to error accumulation and sub-optimal results
Solution Approach 1:
The patent merges 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking into a unified end-to-end trainable framework. The system jointly optimizes all three sub-problems by formulating them as a single differentiable pipeline, where gradients can flow back through all stages simultaneously. This eliminates error accumulation between independent stages and achieves superior tracking accuracy and joint localization.
Solution Approach 2:
The patent introduces a spatio-temporal dimension by formulating the problem in 4D voxel space (3D spatial + 1D temporal). This dimensional transformation allows the system to simultaneously reason about spatial pose relationships and temporal consistency, enabling end-to-end optimization across space and time while maintaining the ability to handle multi-person scenarios with occlusions.
2Reliability
If model-based approaches are used to fit 3D parametric models to estimate 3D shape and pose, then structured prior knowledge is incorporated, but the system becomes sensitive to initialization and local minima
Solution Approach 1:
The patent replaces traditional iterative optimization mechanisms (gradient descent, Levenberg-Marquardt) with a direct regression approach using deep neural networks. Instead of iteratively fitting parametric models, the system learns a direct mapping from 2D image features to 3D pose parameters through end-to-end training. This substitution eliminates sensitivity to initialization and local minima while maintaining the benefits of structured prior knowledge through the network architecture.
3Ease of operation
If piecewise strategies are used to treat 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking as separate stages, then each stage can be optimized independently, but error propagation occurs and robustness to occlusions deteriorates
Solution Approach 1:
The patent implements end-to-end feedback by enabling gradient flow through the entire pipeline from final 3D pose estimation back to initial 2D pose detection. This feedback mechanism allows all stages to be jointly optimized based on the final tracking objective, ensuring that errors in early stages are corrected by subsequent stages and that the system as a whole is robust to occlusions and varying camera views.
Data Source
AI summary
Devices and techniques are generally described for articulated three-dimensional pose tracking. In some examples, a plurality of frames of image data captured by one or more cameras may be received. First feature data representing the plurality of frames of image data may be determined using a backbone network. The first feature data may be projected into three-dimensional (3D) space. In some examples, 3D location data describing respective 3D locations of one or more persons represented by the first feature data projected in the 3D space may be determined. The first feature data and the 3D location data may be sent to a four-dimensional (4D) convolutional neural network (CNN). The 4D CNN may generate second feature data comprising respective 3D representations of the one or more persons. Three dimensional pose data representing articulated 3D pose information for the one or more persons may be generated.