Monocular 3D Human Motion Tracking via Latent Space Dimensionality Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D human motion tracking methods are inefficient and require expensive equipment, complex post-processing, and struggle with occlusions and large limb movements, especially when relying on 2D image sequences and high-dimensional pose state spaces.
Innovation Solution
A method that reduces the dimensionality of the pose state space using a mixture of factor analyzers to generate a prediction model from offline learning, allowing for accurate 3D motion tracking from monocular video sequences without special equipment or markers, by leveraging physical constraints of human motion and non-linear dimensionality reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional particle filtering methods are used for 3D tracking, then tracking accuracy is maintained, but computational efficiency deteriorates due to high dimensionality requiring significant memory and processing power
Solution Approach 1:
The patent transforms the high-dimensional 3D pose state space into a low-dimensional latent space through non-linear dimensionality reduction. By learning a mapping from 3D joint positions to a reduced state space that captures essential motion patterns, the system maintains tracking accuracy while dramatically reducing the number of particles needed for effective filtering.
Solution Approach 2:
The patent changes the parameter representation from full 3D joint coordinates to latent variables in a reduced state space. This parameter transformation preserves the essential dynamics of human motion while reducing dimensionality, allowing particle filtering to operate efficiently with fewer particles while maintaining measurement precision.
2Productivity
If dimensionality reduction is applied to reduce state space, then computational efficiency improves, but tracking accuracy deteriorates when large limb movements occur
Solution Approach 1:
The patent employs a dynamic model that adapts to large limb movements by modeling the temporal evolution of the latent state. The dynamic model captures motion patterns and allows the system to track large movements accurately even in the reduced state space, preventing degradation of tracking precision during dynamic actions.
Solution Approach 2:
The patent performs offline learning to pre-compute the dimensionality reduction mapping and dynamic model parameters from training data. This preliminary action prepares the system to handle various motion patterns efficiently, ensuring that both computational efficiency and tracking accuracy are maintained during online operation even for large limb movements.
3Device complexity
If 2D tracking methods are used to avoid complex equipment, then device complexity is reduced, but the ability to handle occlusions and provide accurate 3D information deteriorates
Solution Approach 1:
The patent creates a virtual 3D model (synthetic silhouette) from the tracked 3D pose and compares it with the actual image silhouette. This copying approach allows the system to use simple monocular video input while maintaining accurate 3D tracking by continuously validating the 3D pose against 2D image observations through silhouette matching.
Solution Approach 2:
The patent introduces a silhouette matching intermediary that bridges 2D image observations and 3D pose estimation. By comparing the projected synthetic silhouette with the actual image silhouette, the system resolves ambiguities in 3D reconstruction and handles occlusions effectively, maintaining 3D accuracy without requiring complex equipment.
Data Source
AI summary
Disclosed is a method and system for efficiently and accurately tracking three-dimensional (3D) human motion from a two-dimensional (2D) video sequence, even when self-occlusion, motion blur and large limb movements occur. In an offline learning stage, 3D motion capture data is acquired and a prediction model is generated based on the learned motions. A mixture of factor analyzers acts as local dimensionality reducers. Clusters of factor analyzers formed within a globally coordinated low-dimensional space makes it possible to perform multiple hypothesis tracking based on the distribution modes. In the online tracking stage, 3D tracking is performed without requiring any special equipment, clothing, or markers. Instead, motion is tracked in the dimensionality reduced state based on a monocular video sequence.