Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

Abstract

Hand pose estimation from a single depth image is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural network, accurate hand pose estimation is still a challenging problem. In this paper we propose a Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by employing tree-structured fully connections. A refined estimation of hand pose is directly regressed by the proposed network and the final hand pose is obtained by utilizing an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.

Accepted by Neurocomputing

References in corpus (5)

• Caffe: Convolutional Architecture for Fast Feature Embedding
• Fast and Robust Hand Tracking Using Detection-Guided Optimization
• Model-based Deep Hand Pose Estimation
• End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data
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Cited by in corpus (25)

• V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
• Pixel-wise Regression: 3D Hand Pose Estimation via Spatial-form Representation and Differentiable Decoder
• A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
• Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images
• Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations
• HandAugment: A Simple Data Augmentation Method for Depth-Based 3D Hand Pose Estimation
• MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks
• Structure-Aware 3D Hourglass Network for Hand Pose Estimation from Single Depth Image
• 3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
• Model-based Hand Pose Estimation for Generalized Hand Shape with Appearance Normalization
• Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
• DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth
• JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
• Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer
• Silhouette-Net: 3D Hand Pose Estimation from Silhouettes
• Patch-based 3D Human Pose Refinement
• HMTNet:3D Hand Pose Estimation from Single Depth Image Based on Hand Morphological Topology
• Dense 3D Regression for Hand Pose Estimation
• Hand range of motion evaluation for Rheumatoid Arthritis patients
• AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation
• Dual Grid Net: hand mesh vertex regression from single depth maps
• Affordance-Aware Handovers with Human Arm Mobility Constraints
• MURAUER: Mapping Unlabeled Real Data for Label AUstERity
• Bi-stream Pose Guided Region Ensemble Network for Fingertip Localization from Stereo Images
• Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose