Hand Pose Estimation Using Synergy Space Under Occlusion
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Solution Overview
Problem
Existing hand pose estimation systems struggle with occlusions during object manipulation, failing to provide accurate and context-aware configurations due to the complexity of human hand articulation and the lack of integration of synergy space and contextual information.
Innovation Solution
A hand pose estimation system utilizing multiple probabilistic encoders that operate in parallel to generate probability distributions within a reduced-dimensional synergy space, incorporating environmental, user-specific, and temporal dynamics to infer likely hand configurations, even under occlusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional inverse kinematics or physics-based models are used to detect hand keypoints and reconstruct poses, then the method is effective when the hand is fully visible, but performance deteriorates during object interaction where occlusions are frequent
Solution Approach 1:
The patent introduces an intermediary probabilistic framework that mediates between incomplete visual observations and the underlying hand pose. Instead of directly mapping visual keypoints to pose (which fails under occlusion), the system uses probability distributions over synergy space as an intermediary representation. This intermediary layer allows the system to reason about hidden states and infer poses even when direct visual evidence is missing, thereby resolving the contradiction between accuracy and occlusion robustness.
Solution Approach 2:
The patent transforms the pose estimation problem from 22-DoF joint space to a lower-dimensional synergy space with fewer parameters. By changing the parameter representation from individual joint angles to synergistic combinations, the system reduces complexity and improves generalization. This parameter transformation enables the model to capture essential hand configurations with fewer parameters, making the estimation more robust to occlusions while maintaining accuracy.
2Adaptability or versatility
If models work directly in the 22-DoF space to capture full hand articulation, then complete hand configurations can be represented, but the complexity increases and not all configurations are physically realizable
Solution Approach 1:
The patent extracts the essential, physically realizable hand configurations from the full 22-DoF space by identifying and removing infeasible or redundant dimensions. Through synergy analysis, the system extracts a smaller set of meaningful parameters (9 synergies) that capture the dominant modes of hand motion. This extraction process eliminates computationally expensive infeasible configurations while preserving the essential articulation patterns needed for realistic hand poses.
Solution Approach 2:
The patent performs a dimensional transformation from the 22-dimensional joint space to a 9-dimensional synergy space. By projecting the high-dimensional pose space onto a lower-dimensional manifold that captures the essential variability of hand motion, the system reduces computational complexity. This dimensionality change maintains coverage of physically realizable configurations while eliminating infeasible ones, thereby resolving the contradiction between configuration coverage and computational complexity.
3Productivity
If existing solutions use reduced-dimensional synergy space to reduce complexity, then computational efficiency improves, but contextual task and scene information is not integrated
Solution Approach 1:
The patent merges multiple information sources (visual observations, task context, scene geometry, and temporal dynamics) into a unified probabilistic model in synergy space. By combining these diverse contextual inputs through probabilistic inference, the system enriches the reduced-dimensional representation with task and scene awareness. This merging process ensures that computational efficiency is maintained while preventing loss of contextual information, as all relevant cues are integrated into the synergy space inference.
Data Source
AI summary
An articulated structure pose estimation system, including: a plurality of synergy space encoders, each configured to generate a respective probability distribution in a synergy space having fewer dimensions than a full joint space, the full joint space corresponding to a multi-degree-of-freedom model of an articulated structure, wherein different ones of the synergy space encoders are configured to encode different contextual or observational information related to articulated structure pose estimation; a synergy heatmap solver configured to: combine the respective probability distributions from the plurality of synergy space encoders to generate a combined probability distribution in the synergy space; and perform probabilistic inference on the combined probability distribution to determine an inferred synergy point; and a synergy decoder configured to decode the inferred synergy point into a pose representation of the articulated structure in the full joint space.