3D Hand Pose Research Articles

An end-to-end framework for unconstrained monocular 3D hand pose estimation

This work addresses the challenging problem of unconstrained 3D hand pose estimation using monocular RGB images. Most of the existing approaches assume some prior knowledge of hand (such as hand locations and side information) is available for 3D hand pose estimation. This restricts their use in unconstrained environments. Therefore, we present an end-to-end framework that robustly predicts hand prior information and accurately infers 3D hand pose by learning ConvNet models while only using keypoint annotations. To enhance the hand detector’s robustness, we propose a novel keypoint-based method to simultaneously predict hand regions and side labels, unlike existing methods that suffer from background color confusion caused by using segmentation or detection-based technology. Moreover, inspired by the human hand’s biological structure, we introduce two geometric constraints directly into the 3D coordinates prediction that further improves its performance. Experimental results show that our proposed framework outperforms the state-of-art methods on standard benchmark datasets while providing robust predictions.

Multi-Level Fusion Net for hand pose estimation in hand-object interaction

This work is about solving a challenging problem of estimating the full 3D hand pose when a hand interacts with an unknown object. Compared to isolated single hand pose estimation, occlusion and interference induced by the manipulated object and the clutter background bring more difficulties for this task. Our proposed Multi-Level Fusion Net focuses on extracting more effective features to overcome these disadvantages by multi-level fusion design with a new end-to-end Convolutional Neural Network (CNN) framework. It takes cropped RGBD data from a single RGBD camera at free viewpoint as input without requiring additional hand–object pre-segmentation and object or hand pre-modeling. Through extensive evaluations on public hand–object interaction dataset, we demonstrate the state-of-the-art performance of our method.

Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image.

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.

Latent Distribution-Based 3D Hand Pose Estimation From Monocular RGB Images

In this article, we propose a novel compressed latent distribution representation for 3D hand pose estimation from monocular RGB images to alleviate the channel correspondence problem. The channel correspondence problem occurs when the 2D and depth coordinates are estimated from independent feature maps, which means the 2D and depth channel sequences may not match during the cross-dataset inference. In contrast, we propose a compressed latent distribution representation that the 2D and depth feature maps for each joint are interconnected and inter-constrained more directly, effectively alleviating the channel correspondence problem and improving cross-dataset performance. Moreover, we design an efficient encoder-decoder network that can maintain the resolution of feature maps to enable better hand feature extraction from monocular RGB images. In this work, the overall pipeline contains two branches: one is the 2D hand pose estimation branch based on a latent heatmap representation (LHR); the other is the 3D hand pose estimation branch based on our proposed latent distribution representation (LDR). In this way, the 2D estimation branch serves as guidance for the 3D branch, which simplifies the optimization of the overall network and results in a more rapid convergence during training. The results on several benchmark datasets (including STB, RHD, and the most recently released InterHand2.6M) demonstrate that our proposed method achieves state-of-the-art (SOTA) performance.

3D Hand Pose Estimation Based on Five-Layer Ensemble CNN.

Estimating accurate 3D hand pose from a single RGB image is a highly challenging problem in pose estimation due to self-geometric ambiguities, self-occlusions, and the absence of depth information. To this end, a novel Five-Layer Ensemble CNN (5LENet) is proposed based on hierarchical thinking, which is designed to decompose the hand pose estimation task into five single-finger pose estimation sub-tasks. Then, the sub-task estimation results are fused to estimate full 3D hand pose. The hierarchical method is of great benefit to extract deeper and better finger feature information, which can effectively improve the estimation accuracy of 3D hand pose. In addition, we also build a hand model with the center of the palm (represented as Palm) connected to the middle finger according to the topological structure of hand, which can further boost the performance of 3D hand pose estimation. Additionally, extensive quantitative and qualitative results on two public datasets demonstrate the effectiveness of 5LENet, yielding new state-of-the-art 3D estimation accuracy, which is superior to most advanced estimation methods.

Spatial-aware stacked regression network for real-time 3D hand pose estimation

Making full use of the spatial information of the depth data is crucial for 3D hand pose estimation from a single depth image. In this paper, we propose a Spatial-aware Stacked Regression Network (SSRN) for fast, robust and accurate 3D hand pose estimation from a single depth image. By adopting a differentiable pose re-parameterization process, our method efficiently encodes the pose-dependent 3D spatial structure of the depth data as spatial-aware representations. Taking such spatial-aware representations as inputs, the stacked regression network utilizes multi-joint spatial context and the 3D spatial relationship between the estimated pose and the depth data to predict a refined hand pose. To further improve the estimation accuracy, we adopt a spatial attention mechanism to reduce the influence of irrelevant features for pose regression. In order to improve the speed of the network, we propose a cross-stage self-distillation mechanism to distill knowledge within the network itself. Experiments on four datasets show that our proposed method achieves state-of-the-art accuracy with high running speed around 330 FPS on a single GPU and 35 FPS on a single CPU.

A framework for automatic hand range of motion evaluation of rheumatoid arthritis patients

We propose a framework for evaluation of finger movement patterns on Rheumatoid Arthritis patients: flexion, extension, abduction and adduction. The framework uses a state-of-the-art 3D hand pose estimation method that runs in real-time, allowing users to visualize 3D skeleton tracking results at the same time as the depth images are acquired. We compute flexion and abduction angles from the obtained skeleton pose parameters. We performed data acquisition from a cohort of patients and a control set and compared the angles from those two sets of people. An analysis using time series similarity with frequency domain descriptors is adopted to characterize the movement patterns for flexion/extension. We performed classification experiments using these descriptors, thus distinguishing movement sequences of hands with rheumatoid arthritis from healthy hands. The descriptors used in the classification experiment were effective and reached average results of 89 % in scenarios of unseen subjects, and an average of 82 % in experiments with sample synthesis that allow a more robust statistical performance evaluation. Our framework allows the characterization of the current state of the disorder in each patient, with minimal intervention and reduced evaluation time.

Hand Pose Estimation in the Task of Egocentric Actions

In this article we tackle the problem of hand pose estimation when the hand is interacting with various objects from egocentric viewpoint. This entails a frequent occlusion of parts of the hand by the object and also self-occlusions of the hand. We use a Voxel-to-Voxel approach to obtain hypotheses of the hand joint locations, ensemble the hypotheses and use several post-processing strategies to improve on the results. We utilize models of prior hand pose in the form of Truncated Singular Value Decomposition (SVD) and the temporal context to produce refined hand joint locations. We present an ablation study of the methods to show the influence of individual features of the post-processing. With our method we were able to constitute state-of-the-art results on the HANDS19 Challenge: Task 2 - Depth-Based 3D Hand Pose Estimation while Interacting with Objects, with precision on unseen test data of 33.09 mm.

Differentiable Spatial Regression: A Novel Method for 3D Hand Pose Estimation

3D Hand pose estimation from a single depth image is an essential topic in computer vision and human-computer interaction. Although the rising of deep learning method boosts the accuracy a lot, the problem is still hard to solve due to the complex structure of the human hand. Existing methods with deep learning either lose spatial information of hand structure or lack a direct supervision of joint coordinates. In this paper, we propose a novel Pixel-wise Regression method, which use spatial-form representation (SFR) and differentiable decoder (DD) to solve the two problems. To use our method, we build a model, in which we design a particular SFR and its correlative DD which divided the 3D joint coordinates into two parts, plane coordinates and depth coordinates and use two modules named Plane Regression (PR) and Depth Regression (DR) to deal with them respectively. We conduct an ablation experiment to show the method we proposed achieve better results than the former methods. We also make an exploration on how different training strategies influence the learned SFRs and results. The experiment on three public datasets demonstrates that our model is comparable with the existing state-of-the-art models and in one of them our model can reduce mean 3D joint error by 25%.

LPPM-Net: Local-aware point processing module based 3D hand pose estimation for point cloud

3D hand pose estimation by taking point cloud as input has been paid more and more attention recently. In this paper, a new module for point cloud processing, named Local-aware Point Processing Module (LPPM), is designed. With the ability to extract local information, it is permutation invariant w.r.t. neighboring points in input point cloud and is an independent module that is easy to be implemented and flexible to construct point cloud network. Based on this module, a LPPM-Net is constructed to estimate 3D hand pose. In order to normalize orientations of the point cloud as well as to maintain diversity properly in a controllable manner, we transform point cloud into an oriented bounding box coordinate system (OBB C.S.) and then rotate it randomly around the principal axis when training. In addition, a simple but effective technique called sampling ensemble is used in the test stage, which compensates for the resolution degradation caused by downsampling and improves the performance without extra parameters. We evaluate the proposed method on three public hand datasets: NYU, ICVL, and MSRA. Results show that our approach has a competitive performance on the three datasets.

Accurate 3D hand pose estimation network utilizing joints information

In this paper, a method is proposed to improve the accuracy of 3D hand pose estimation. The existing methods make poor use of the depth information of hand joints and have difficulties of estimating the 3D coordinates accurately. To solve this problem, a method that utilizing the information between adjacent joints of each finger is proposed to estimate the depth coordinates of joints. In order to make full use of 2D information for depth estimation, this paper divides hand pose estimation into two sub-tasks (2D hand joints estimation and depth estimation). In depth estimation, a multi-stage network is proposed. We first estimate the depth of a part of hand joints, and then with the help of it and 2D information, the depth coordinates of adjacent joints can be well estimated. The method proposed in this paper has been proved to be effective on three public hand pose datasets through Self-comparisons. Compared with the methods that based on 2D CNN, our method achieves state-of-the-art performance on ICVL and NYU datasets, and also has a good result on MSRA dataset.

3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

Hand pose estimation from 3D data is a key challenge in computer vision as well as an essential step for human–computer interaction. A lot of deep learning-based hand pose estimation methods have made significant progress but give less consideration to the inner interactions of input data, especially when consuming hand point clouds. Therefore, this paper proposes an end-to-end capsule-based hand pose estimation network (Capsule-HandNet), which processes hand point clouds directly with the consideration of structural relationships among local parts, including symmetry, junction, relative location, etc. Firstly, an encoder is adopted in Capsule-HandNet to extract multi-level features into the latent capsule by dynamic routing. The latent capsule represents the structural relationship information of the hand point cloud explicitly. Then, a decoder recovers a point cloud to fit the input hand point cloud via a latent capsule. This auto-encoder procedure is designed to ensure the effectiveness of the latent capsule. Finally, the hand pose is regressed from the combined feature, which consists of the global feature and the latent capsule. The Capsule-HandNet is evaluated on public hand pose datasets under the metrics of the mean error and the fraction of frames. The mean joint errors of Capsule-HandNet on MSRA and ICVL datasets reach 8.85 mm and 7.49 mm, respectively, and Capsule-HandNet outperforms the state-of-the-art methods on most thresholds under the fraction of frames metric. The experimental results demonstrate the effectiveness of Capsule-HandNet for 3D hand pose estimation.

A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods

The field of 3D hand pose estimation has been gaining a lot of attention recently, due to its significance in several applications that require human-computer interaction (HCI). The utilization of technological advances, such as cost-efficient depth cameras coupled with the explosive progress of Deep Neural Networks (DNNs), has led to a significant boost in the development of robust markerless 3D hand pose estimation methods. Nonetheless, finger occlusions and rapid motions still pose significant challenges to the accuracy of such methods. In this survey, we provide a comprehensive study of the most representative deep learning-based methods in literature and propose a new taxonomy heavily based on the input data modality, being RGB, depth, or multimodal information. Finally, we demonstrate results on the most popular RGB and depth-based datasets and discuss potential research directions in this rapidly growing field.

Simultaneous 3D hand detection and pose estimation using single depth images

Abstract In this paper, we investigate 3D hand pose estimation using single depth images. On the one hand, accurate hand localization is a crucial factor for pose estimation. On the other hand, multi-task learning methods have achieved great success in visual recognition tasks. Therefore, in this paper we proposed to simultaneously detect the hand location and estimate its 3D pose in a multi-task learning framework. We used 3D region proposal for 3D pose estimation, which searches possible hand locations in the 3D space. In the experimental part, the proposed method is evaluated on several benchmark datasets and shown it is comparable to most existing 3D hand pose estimation methods.

Learning a deep predictive coding network for a semi-supervised 3D-hand pose estimation

In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model &#x0028 LDTM &#x0029 which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.

Cascaded Hierarchical CNN for RGB-Based 3D Hand Pose Estimation

3D hand pose estimation can provide basic information about gestures, which has an important significance in the fields of Human-Machine Interaction (HMI) and Virtual Reality (VR). In recent years, 3D hand pose estimation from a single depth image has made great research achievements due to the development of depth cameras. However, 3D hand pose estimation from a single RGB image is still a highly challenging problem. In this work, we propose a novel four-stage cascaded hierarchical CNN (4CHNet), which leverages hierarchical network to decompose hand pose estimation into finger pose estimation and palm pose estimation, extracts separately finger features and palm features, and finally fuses them to estimate 3D hand pose. Compared with direct estimation methods, the hand feature information extracted by the hierarchical network is more representative. Furthermore, concatenating various stages of the network for end-to-end training can make each stage mutually beneficial and progress. The experimental results on two public datasets demonstrate that our 4CHNet can significantly improve the accuracy of 3D hand pose estimation from a single RGB image.

Graph-Based CNNs With Self-Supervised Module for 3D Hand Pose Estimation From Monocular RGB

Hand pose estimation in 3D space from a single RGB image is a highly challenging problem due to self-geometric ambiguities, diverse texture, viewpoints, and self-occlusions. Existing work proves that a network structure with multi-scale resolution subnets, fused in parallel can more effectively shows the spatial accuracy of 2D pose estimation. Nevertheless, the features extracted by traditional convolutional neural networks cannot efficiently express the unique topological structure of hand key points based on discrete and correlated properties. Some applications of hand pose estimation based on traditional convolutional neural networks have demonstrated that the structural similarity between the graph and hand key points can improve the accuracy of the 3D hand pose regression. In this paper, we design and implement an end-to-end network for predicting 3D hand pose from a single RGB image. We first extract multiple feature maps from different resolutions and make parallel feature fusion, and then model a graph-based convolutional neural network module to predict the initial 3D hand key points. Next, we use 2D spatial relationships and 3D geometric knowledge to build a self-supervised module to eliminate domain gaps between 2D and 3D space. Finally, the final 3D hand pose is calculated by averaging the 3D hand poses from the GCN output and the self-supervised module output. We evaluate the proposed method on two challenging benchmark datasets for 3D hand pose estimation. Experimental results show the effectiveness of our proposed method that achieves state-of-the-art performance on the benchmark datasets.

Neural hand reconstruction using an RGB Image

Background This study presents a neural hand reconstruction method for monocular 3D hand pose and shape estimation. Methods Alternate to directly representing hand with 3D data, a novel UV position map is used to represent a hand pose and shape with 2D data that maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from a single image. To train this network with inadequate ground truth training pairs, we propose a novel MANOReg module that employs MANO model as a prior shape to constrain high-dimensional space of the UV p sition map. Results The quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.

HMTNet: 3D Hand Pose Estimation From Single Depth Image Based on Hand Morphological Topology

Thanks to the rapid development of CNNs and depth sensors, great progress has been made in 3D hand pose estimation. Nevertheless, it is still far from being solved for its cluttered circumstance and severe self-occlusion of hand. In this paper, we propose a method that takes advantage of human hand morphological topology (HMT) structure to improve the pose estimation performance. The main contributions of our work can be listed as below. Firstly, in order to extract more powerful features, we concatenate original and last layer of initial feature extraction module to preserve hand information better. Next, regression module inspired from hand morphological topology is proposed. In this submodule, we design a tree-like network structure according to hand joints distribution to make use of high order dependency of hand joints. Lastly, we conducted sufficient ablation experiments to verify our proposed method on each dataset. Experimental results on three popular hand pose dataset show superior performance of our method compared with the state-of-the-art methods. On ICVL and NYU dataset, our method outperforms great improvement over 2D state-of-the-art methods. On MSRA dataset, our method achieves comparable accuracy with the state-of-the-art methods. To summarize, our method is the most efficient method which can run at 220:7 fps on a single GPU compared with approximate accurate methods at present. The code will be available at.

Real-Time Energy Efficient Hand Pose Estimation: A Case Study.

The estimation of human hand pose has become the basis for many vital applications where the user depends mainly on the hand pose as a system input. Virtual reality (VR) headset, shadow dexterous hand and in-air signature verification are a few examples of applications that require to track the hand movements in real-time. The state-of-the-art 3D hand pose estimation methods are based on the Convolutional Neural Network (CNN). These methods are implemented on Graphics Processing Units (GPUs) mainly due to their extensive computational requirements. However, GPUs are not suitable for the practical application scenarios, where the low power consumption is crucial. Furthermore, the difficulty of embedding a bulky GPU into a small device prevents the portability of such applications on mobile devices. The goal of this work is to provide an energy efficient solution for an existing depth camera based hand pose estimation algorithm. First, we compress the deep neural network model by applying the dynamic quantization techniques on different layers to achieve maximum compression without compromising accuracy. Afterwards, we design a custom hardware architecture. For our device we selected the FPGA as a target platform because FPGAs provide high energy efficiency and can be integrated in portable devices. Our solution implemented on Xilinx UltraScale+ MPSoC FPGA is 4.2× faster and 577.3× more energy efficient than the original implementation of the hand pose estimation algorithm on NVIDIA GeForce GTX 1070.