We present a novel solution to the problem of recovering and tracking the 3D position, orientation and full articulation of a human hand from markerless visual observations obtained by a Kinect sensor. We treat this as an optimization problem, seeking for the hand model parameters that minimize the discrepancy between the appearance and 3D structure of hypothesized instances of a hand model and actual hand observations. This optimization problem is effectively solved using a variant of Particle Swarm Optimization (PSO). The proposed method does not require special markers and/or a complex image acquisition setup. Being model based, it provides continuous solutions to the problem of tracking hand articulations. Extensive experiments with a prototype GPU-based implementation of the proposed method demonstrate that accurate and robust 3D tracking of hand articulations can be achieved in near real-time (15Hz).

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We propose a method that relies on markerless visual observations to track the full articulation of two hands that interact with each-other in a complex, unconstrained manner. We formulate this as an optimization problem whose 54-dimensional parameter space represents all possible configurations of two hands, each represented as a kinematic structure with 26 Degrees of Freedom (DoFs). To solve this problem, we employ Particle Swarm Optimization (PSO), an evolutionary, stochastic optimization method with the objective of finding the two-hands configuration that best explains observations provided by an RGB-D sensor. To the best of our knowledge, the proposed method is the first to attempt and achieve the articulated motion tracking of two strongly interacting hands. Extensive quantitative and qualitative experiments with simulated and real world image sequences demonstrate that an accurate and efficient solution of this problem is indeed feasible.

In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-ofthe-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.

We present a method for the real-time estimation of the full 3D pose of one or more human hands using a single commodity RGB camera. Recent work in the area has displayed impressive progress using RGBD input. However, since the introduction of RGBD sensors, there has been little progress for the case of monocular color input. We capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D hand pose estimation in unrestricted scenarios. More specifically, given an RGB image and the relevant camera calibration information, we employ a state-of-the-art detector to localize hands. Given a crop of a hand in the image, we run the pretrained network of OpenPose for hands to estimate the 2D location of hand joints. Finally, non-linear least-squares minimization fits a 3D model of the hand to the estimated 2D joint positions, recovering the 3D hand pose. Extensive experimental results provide comparison to the state of the art as well as qualitative assessment of the method in the wild.

We present a new method for tracking the 3D position, global orientation and full articulation of human hands. Inspired by recent advances in model-based, hypothesizeand-test methods, the high-dimensional parameter space of hand configurations is explored with a novel evolutionary optimization technique. The proposed method capitalizes on the fact that the quasi-random samples of the Sobol sequence have low discrepancy and exhibit a more uniform coverage of the sampled space compared to random samples obtained from the uniform distribution. The method has been tested for the problems of tracking the articulation of a single hand (27D parameter space) and two hands (54D space). Extensive experiments have been carried out with synthetic and real data, in comparison with state of the art methods. The quantitative evaluation shows that for cases of limited computational resources, the new approach achieves a speed-up of four (single hand tracking) and eight (two hands tracking) without compromising tracking accuracy. Interestingly, the proposed method is preferable compared to the state of the art either in the case of limited computational resources or in the case of more complex (i.e., higher dimensional) problems, thus improving the applicability of the method in a number of application domains.

Figure 1: A) Digits is a wrist-worn sensor for freehand 3D interactions on the move. By instrumenting only the wrist, the user's entire hand is left to interact freely without wearing a data glove. B and C) Digits recovers the full 3D pose of a user's hand. D) Spatial interactions using a mobile phone and Digits. ABSTRACTDigits is a wrist-worn sensor that recovers the full 3D pose of the user's hand. This enables a variety of freehand interactions on the move. The system targets mobile settings, and is specifically designed to be low-power and easily reproducible using only off-the-shelf hardware. The electronics are self-contained on the user's wrist, but optically image the entirety of the user's hand. This data is processed using a new pipeline that robustly samples key parts of the hand, such as the tips and lower regions of each finger. These sparse samples are fed into new kinematic models that leverage the biomechanical constraints of the hand to recover the 3D pose of the user's hand. The proposed system works without the need for full instrumentation of the hand (for example using data gloves), additional sensors in the environment, or depth cameras which are currently prohibitive for mobile scenarios due to power and form-factor considerations. We demonstrate the utility of Digits for a variety of application scenarios, including 3D spatial interaction with mobile devices, eyes-free interaction on-the-move, and gaming. We conclude with a quantitative and qualitative evaluation of our system, and discussion of strengths, limitations and future work.

Abstract-The main contribution of this paper is a probabilistic method for predicting human manipulation intention from image sequences of human-object interaction. Predicting intention amounts to inferring the imminent manipulation task when human hand is observed to have stably grasped the object. Inference is performed by means of a probabilistic graphical model that encodes object grasping tasks over the 3D state of the observed scene. The 3D state is extracted from RGB-D image sequences by a novel vision-based, markerless hand-object 3D tracking framework. To deal with the high-dimensional statespace and mixed data types (discrete and continuous) involved in grasping tasks, we introduce a generative vector quantization method using mixture models and self-organizing maps. This yields a compact model for encoding of grasping actions, able of handling uncertain and partial sensory data. Experimentation showed that the model trained on simulated data can provide a potent basis for accurate goal-inference with partial and noisy observations of actual real-world demonstrations. We also show a grasp selection process, guided by the inferred human intention, to illustrate the use of the system for goal-directed grasp imitation.