3D Hand Pose Estimation Using Temporal Self-Supervision
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Solution Overview
Problem
Estimating 3D hand poses from RGB images is challenging due to the difficulty in annotating 3D poses, with limited available datasets and the need for explicit 3D annotations, which complicates training deep models.
Innovation Solution
A self-supervised learning model, called temporal-aware self-supervised network (TASSN), uses temporal consistency constraints to estimate 3D hand poses and meshes from videos annotated with only 2D key frame positions, leveraging optical flow and graph convolutional networks to infer 3D hand meshes without explicit 3D annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit 3D annotations are used for training deep models, then measurement precision of hand pose estimation is improved, but device complexity and data annotation difficulty increase significantly
Solution Approach 1:
The system performs self-supervised learning by automatically generating supervision signals from optical flow and temporal consistency constraints without requiring manual 3D annotations. The model learns to estimate 3D hand poses by exploiting temporal relationships in video sequences, where the system serves itself by creating its own training labels from readily available 2D video data.
Solution Approach 2:
Optical flow serves as an intermediary that bridges 2D video frames to provide implicit 3D motion information. By computing optical flow between consecutive frames and enforcing temporal consistency, the system indirectly obtains supervision signals that guide 3D pose estimation without direct 3D annotation input.
2Measurement precision
If 3D annotated datasets are collected for training, then estimation accuracy is improved, but loss of time and productivity decrease due to manual annotation requirements
Solution Approach 1:
The system eliminates manual annotation time by performing self-supervised learning. Training data is automatically generated from unannotated video sequences through optical flow computation and temporal consistency enforcement, allowing the model to learn 3D pose estimation without human intervention in the annotation process.
Solution Approach 2:
The system performs preliminary computation of optical flow and temporal consistency constraints on raw video data before actual pose estimation. This preliminary processing creates ready-to-use supervision signals that can be directly applied to train the model, eliminating the need for time-consuming manual 3D annotation during data preparation.
3Reliability
If temporal consistency constraints are enforced, then reliability of 3D pose estimation is improved, but device complexity increases due to additional computational requirements
Solution Approach 1:
The system enforces temporal consistency by comparing predicted 3D poses across consecutive frames with optical flow-derived motion constraints. This feedback mechanism adjusts the pose estimates to ensure temporal coherence, improving reliability by preventing temporally inconsistent predictions while using computationally efficient optical flow comparisons.
Data Source
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AI summary
A method, computer program, and computer system is provided for estimating three-dimensional hand poses in images. Data corresponding to two hand images is receive, and an optical flow value corresponding to a change in a hand gesture in the received hand image data is calculate. A heat map is generated based on the calculated optical flow, and a hand mesh map is estimated based on the generated heat map. A hand pose present within the hand images is determined based on the estimated hand mesh map.