3D Hand Pose Estimation Using Depth-Image Guided GAN
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Solution Overview
Problem
Estimating 3D hand poses from RGB images is challenging due to the ambiguity in inferring depth information, as existing methods require paired RGB and depth maps for training, limiting their applicability in applications like computer vision and virtual reality.
Innovation Solution
A Depth-image Guided GAN (DGGAN) is used to generate realistic depth maps from RGB images, allowing for the estimation of 3D hand poses without the need for simultaneous depth map input, thereby regularizing the pose estimation model and eliminating the requirement for paired data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If paired RGB and depth maps are used for training, then measurement precision of hand pose estimation is improved, but device complexity and data requirements increase
Solution Approach 1:
The patent uses a Generative Adversarial Network (GAN) to synthesize realistic depth maps from RGB images. The generator creates depth map copies that mimic real depth maps, allowing the system to train and operate using only RGB images while maintaining the measurement precision that would normally require actual depth map inputs.
Solution Approach 2:
The patent introduces a depth map generation module as an intermediary between RGB images and the hand pose estimation network. This intermediary synthesizes depth information from RGB inputs, eliminating the need for direct depth map input while providing the depth cues necessary for accurate 3D pose estimation.
2Measurement precision
If depth maps are required for training, then measurement precision is improved, but ease of operation deteriorates due to simultaneous input requirements
Solution Approach 1:
The GAN-based depth map generator creates synthetic depth representations from standard RGB images, allowing users to operate the system with simple single-image inputs while achieving the measurement precision that would normally require complex paired depth map inputs.
3Manufacturing precision
If paired RGB and depth data are used, then manufacturing precision of pose estimation model is improved, but loss of time increases due to data processing requirements
Solution Approach 1:
The system generates depth map copies on-the-fly from RGB images during both training and inference, eliminating the need for time-consuming paired data processing while maintaining model training precision. The synthesized depth maps are produced instantly from the available RGB input.
Solution Approach 2:
The depth map generation module performs preliminary synthesis of depth information before pose estimation, converting RGB images into depth-representative formats in advance of the main estimation process, thereby streamlining the overall data processing pipeline.
Data Source
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AI summary
A computer-implemented method, computer readable storage medium, and computer system is provided for estimating three-dimensional (3D) hand poses in images by receiving data corresponding to a hand image, generating a depth map corresponding to the received hand image data, and estimating a hand pose from the received hand image data and the generated depth map.