Efficient Hand Pose Estimation from a Single Depth Image

Depth images are widely used in 3D head pose estimation and face reconstruction. The device-specific noise and the lack of textual constraints pose a major problem for estimating a nonrigid deformable face from a single noisy depth image. In this article, we present a deep neural network-based framework to infer a 3D face consistent with a single depth image captured by a consumer depth camera Kinect. Confronted with a lack of annotated depth images with facial parameters, we utilize the bidirectional CycleGAN-based generator for denoising and noisy image simulation, which helps to generalize the model learned from synthetic depth images to real noisy ones. We generate the code regressors in the source (synthetic) and the target (noisy) depth image domains and present a fusion scheme in the parametric space for 3D face inference. The proposed multi-level shape consistency constraint, concerning the embedded features, depth maps, and 3D surfaces, couples the code regressor and the domain adaptation, avoiding shape distortions in the CycleGAN-based generators. Experiments demonstrate that the proposed method is effective in depth-based 3D head pose estimation and expressive face reconstruction compared with the state-of-the-art.

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.

Simple and effective deep hand shape and pose regression from a single depth image

Deep hand pose estimation from single depth image plays a significant role in human-computer interaction. This paper proposes a novel method based on multiple transfer net to estimate hand pose utilizing only single-channel depth photos. A channel extending process for original single channel depth image is implemented to extend hand and hand palm regions and match the input format of a pre-trained network and fully utilize the parameters. A multiple transfer network refinement for the previous convolutional neural network is made to obtain various different feature maps. Also, a region ensemble is used to merge all output feature maps and integrate the results. The experimental results demonstrate that the proposed method outperforms state-of-art results with considerable accuracy on the NYU [1] and ICVL [2] datasets.

Depth images play an important role and are popularly used in many computer vision tasks recently. However, the limited resolution of the depth image has been hindering its further applications. To address this problem, we propose a novel dual sparsity model based single depth image super resolution algorithm, with a single low-resolution depth image as input. We formulate this problem by combining the recently developed analysis model and synthesis model exploiting the sparsity of analyzed vectors and the sparse coefficients respectively. The analysis operator and dictionaries are trained over extensive samples separately. We show that our model clearly outperforms state-of-the-art methods on the widely used Middlebury 2007 datasets both quantitatively and visually.

Content-based 3D shape retrieval is an important problem in computer vision. Traditional retrieval interfaces require a 2D sketch or a manually designed 3D model as the query, which is difficult to specify and thus not practical in real applications. With the recent advance in low-cost 3D sensors such as Microsoft Kinect and Intel Realsense, capturing depth images that carry 3D information is fairly simple, making shape retrieval more practical and user-friendly. In this paper, we study the problem of cross-domain 3D shape retrieval using a single depth image from low-cost sensors as the query to search for similar human designed CAD models. We propose a novel method using an ensemble of autoencoders in which each autoencoder is trained to learn a compressed representation of depth views synthesize d from each database object. By viewing each autoencoder as a probabilistic model, a likelihood score can be derived as a similarity measure. A domain adaptation layer is built on top of autoencoder outputs to explicitly address the cross-domain issue (between noisy sensory data and clean 3D models) by incorporating training data of sensor depth images and their category labels in a weakly supennsed learning formulation. Experiments using real-world depth images and a large-scale CAD dataset demonstrate the effectiveness of our approach, which offers significant improvements over state-of-the-art 3D shape retrieval methods.

In this paper we present the latent regression forest (LRF), a novel framework for real-time, 3D hand pose estimation from a single depth image. Prior discriminative methods often fall into two categories: holistic and patch-based. Holistic methods are efficient but less flexible due to their nearest neighbour nature. Patch-based methods can generalise to unseen samples by consider local appearance only. However, they are complex because each pixel need to be classified or regressed during testing. In contrast to these two baselines, our method can be considered as a structured coarse-to-fine search, starting from the centre of mass of a point cloud until locating all the skeletal joints. The searching process is guided by a learnt latent tree model which reflects the hierarchical topology of the hand. Our main contributions can be summarised as follows: (i) Learning the topology of the hand in an unsupervised, data-driven manner. (ii) A new forest-based, discriminative framework for structured search in images, as well as an error regression step to avoid error accumulation. (iii) A new multi-view hand pose dataset containing 180 K annotated images from 10 different subjects. Our experiments on two datasets show that the LRF outperforms baselines and prior arts in both accuracy and efficiency.

Human pose reconstruction using depth images has received much attention for human-centric applications. Body-part labeling at pixel-level has shown to be efficient for human pose reconstruction. This paper presents an accurate human pose reconstruction method from a single depth image by combining body-part labeling and nearest pose-matching techniques. New pixel-level depth difference and local curvature-encoding features are introduced to provide more contextual depth information for pixel-level body-part labeling. To reduce the misclassification error, inspired by pose-matching techniques, a corrective step is also proposed. The method extracts depth region proposals from a reference pose and finds the best match using PCT coefficients to correct uncertain labels. Tests on a set of synthetic and natural depth poses showed improved accuracy of body-part labeling compared to the state-of-the-art methods. In addition, in comparison with the previous methods and the Kinect camera, an improved accuracy for human range of motion measurement was obtained .

Semantic Scene Completion (SSC) is a task that simultaneously predicts the occupancy and semantic labels of the environment. Compared with separate processing, SSC leverages the coupled nature of scene completion and semantic segmentation. Although this multitask integration can utilize complementarity and correlation between tasks, it also increases the training difficulty. To address this, in this paper, we propose a Semantic Decoupling based Semantic Scene Completion (SD-SSC) network from a single depth image. The semantic segmentation task is decoupled from the semantic scene completion task, and we use 2D and 3D semantic supervision to simplify the scene completion task and improve SSC performance. Specifically, our network first performs 2D semantic segmentation on the depth image and transforms features into 3D voxel space as semantic priors. Then, the 3D SSC is performed based on the voxel features and the flipped Truncated Signed Distance Field (f-TSDF). We use multi-scale 3D semantic supervision to further enhance the semantic information and fuse semantic and geometric features through the Planar Attention Fusion Module (PAFM) to obtain accurate SSC results. The proposed SD-SSC network achieves state-of-the-art performance on the NYU dataset (51.1% mIoU) and the NYUCAD dataset (61.9% mIoU) among all single depth-image based methods. It is even better than most RGB-D fusion-based SSC methods. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Saturday-xql/SD-SSC</uri>.

We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network architecture with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100 K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.

The existing single depth image super-resolution (SR) methods suppose that the image to be interpolated is noise free. However, the supposition is invalid in practice because noise will be inevitably introduced in the depth image acquisition process. In this paper, we address the problem of image denoising and SR jointly based on designing sparse graphs that are useful for describing the geometric structures of data domains. In our method, we first cluster similar patches in a noisy depth image and compute an average patch. Different from the majority of the graph Fourier transform (GFT) that assumed an underlying 4-connected graph structure with vertical and horizontal edges only, we select more general sparse graph structures and edges weights based on the difference of the blocks' structure tensors. For the average patch, a graph template with edges orthogonal to the principal gradient is designed. Finally, the graph based transform (GBT) dictionary is learned from the derived correlation graph for signal representation. As shown in our experimental results, the proposed method obtains a lot of improvement in performance.

\n Hand pose estimation is a challenging problem in computer vision with a wide range of applications, especially in human-computer interface. With the development of inexpensive consumer-level depth cameras and the evolution on deep learning techniques, the current state-of-art in the problem is continuously developing and several new methods have been proposed in recent years. Those methods are mostly data-driven and reach good results in standard datasets such as NYU, ICVL and HANDS17. An application that would benefit from the use of computer vision techniques is hand occupational therapy. In chronic diseases like rheumatoid arthritis (RA), the evaluation of the hand functional state is fundamental for the treatment and prevention of finger deformities. One of the procedures for deformity diagnosis is the measurement of movement angles i.e. flexion/extension and abduction/addution, made using goniometers in a process that can be time-consuming and invasive for the patient. The main proposal of this PhD is to fill a gap in the literature by proposing and evaluating the viability of using a framework composed of a 3D low-cost sensor and a 3D hand pose estimation state-of-art method for automatic assessment of rheumatoid arthritis patients. Given depth maps as input, our framework estimates 3D hand joint positions using a deep convolutional neural network. The proposed pose estimation algorithm can be executed in real-time, allowing users to visualise 3D skeleton tracking results at the same time as the depth images are acquired. Once 3D joint poses are obtained, our framework estimates flexion/extension and abduction/adduction angles by applying computational geometry oper- ations. The absence of public datasets with RA patients in the literature makes the estimation of hand poses of patients a challenge for computer vision data-driven methods. We therefore proposed a protocol to acquire new data from groups of patients and control. We performed experiments of identification of RA patients and control sets and also performed comparison with goniometer data. Results show that a method based on Fourier descriptors is able to perform automatic discrimination of hands with Rheumatoid Arthritis (RA) and healthy pa- tients. The angle between joints can be used as an indicative of current movement capabilities and function. The acquisition is much easier, non-invasive and patient-friendly, significantly reducing the evaluation time and offering real-time data for the dynamic movement.\n

In recent years, numerous consumer devices have emerged that are capable of capturing 3D point data originating from depth images. Many computer vision tasks, such as object recognition, environment mapping, augmented reality, and more, rely on accurately registering 3D point sets. One method to compute this registration is to use 3D local feature descriptors for a coarse alignment, and to further refine the alignment with a variant of the Iterative Closest Point algorithm. While robust feature descriptors work well for this approach, online computation for all points in a single depth image is typically intractable. In this work, a method to facilitate real-time 3D registration by performing descriptor extraction on depth images using a Fully Convolutional Network (FCN) is presented. The input to this method is a raw depth image and results in a 33-bin descriptor for each pixel, enabling a general-purpose 3D registration process that doesn't require future network retraining and refinement. Experimental results on consumer hardware demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of computation time and approaches depth sensor frame capture times, with only a slight reduction in descriptiveness.

Top-down model fitting for hand pose recovery in sequences of depth images

This paper proposes a universal framework, called OVE6D, for model-based 6D object pose estimation from a single depth image and a target object mask. Our model is trained using purely synthetic data rendered from ShapeNet, and, unlike most of the existing methods, it generalizes well on new real-world objects without any fine-tuning. We achieve this by decomposing the 6D pose into viewpoint, in-plane rotation around the camera optical axis and translation, and introducing novel lightweight modules for estimating each component in a cascaded manner. The resulting network contains less than 4M parameters while demon-strating excellent performance on the challenging T-LESS and Occluded LINEMOD datasets without any dataset-specific training. We show that OVE6D outperforms some contemporary deep learning-based pose estimation methods specifically trained for individual objects or datasets with real-world training data. The implementation is available at https://github.com/dingdingcai/OVE6D-pose.