3D hand tracking.

The hand is often considered as one of the most natural and intuitive interaction modalities for human-to-human interaction. In human-computer interaction (HCI), proper 3D hand tracking is the first step in developing a more intuitive HCI system which can be used in applications such as gesture recognition, virtual object manipulation and gaming. However, accurate 3D hand tracking, remains a challenging problem due to the hand’s deformation, appearance similarity, high inter-finger occlusion and complex articulated motion. Further, 3D hand tracking is also interesting from a theoretical point of view as it deals with three major areas of computer vision- segmentation (of hand), detection (of hand parts), and tracking (of hand). This thesis proposes a region-based skin color detection technique, a model-based and an appearance-based 3D hand tracking techniques to bring the human-computer interaction applications one step closer. All techniques are briefly described below. Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on individual pixels. This thesis presents a new region-based technique for skin color detection which outperforms the current state-of-the-art pixel-based skin color detection technique on the popular Compaq dataset (Jones & Rehg 2002). The proposed technique achieves 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images. Hand tracking is not a trivial task as it requires tracking of 27 degreesof- freedom of hand. Hand deformation, self occlusion, appearance similarity and irregular motion are major problems that make 3D hand tracking a very challenging task. This thesis proposes a model-based 3D hand tracking technique, which is improved by using proposed depth-foreground-background ii feature, palm deformation module and context cue. However, the major problem of model-based techniques is, they are computationally expensive. This can be overcome by discriminative techniques as described below. Discriminative techniques (for example random forest) are good for hand part detection, however they fail due to sensor noise and high interfinger occlusion. Additionally, these techniques have difficulties in modelling kinematic or temporal constraints. Although model-based descriptive (for example Markov Random Field) or generative (for example Hidden Markov Model) techniques utilize kinematic and temporal constraints well, they are computationally expensive and hardly recover from tracking failure. This thesis presents a unified framework for 3D hand tracking, using the best of both methodologies, which out performs the current state-of-the-art 3D hand tracking techniques. The proposed 3D hand tracking techniques in this thesis can be used to extract accurate hand movement features and enable complex human machine interaction such as gaming and virtual object manipulation

Decaf: Monocular Deformation Capture for Face and Hand Interactions

Existing methods for 3D tracking from monocular RGB videos predominantly consider articulated and rigid objects. Modelling dense non-rigid object deformations in this setting remained largely unaddressed so far, although such effects can improve the realism of the downstream applications such as AR/VR and avatar communications. This is due to the severe ill-posedness of the monocular view setting and the associated challenges. While it is possible to naively track multiple non-rigid objects independently using 3D templates or parametric 3D models, such an approach would suffer from multiple artefacts in the resulting 3D estimates such as depth ambiguity, unnatural intra-object collisions and missing or implausible deformations. Hence, this paper introduces the first method that addresses the fundamental challenges depicted above and that allows tracking human hands interacting with human faces in 3D from single monocular RGB videos. We model hands as articulated objects inducing non-rigid face deformations during an active interaction. Our method relies on a new hand-face motion and interaction capture dataset with realistic face deformations acquired with a markerless multi-view camera system. As a pivotal step in its creation, we process the reconstructed raw 3D shapes with position-based dynamics and an approach for non-uniform stiffness estimation of the head tissues, which results in plausible annotations of the surface deformations, hand-face contact regions and head-hand positions. At the core of our neural approach are a variational auto-encoder supplying the hand-face depth prior and modules that guide the 3D tracking by estimating the contacts and the deformations. Our final 3D hand and face reconstructions are realistic and more plausible compared to several baselines applicable in our setting, both quantitatively and qualitatively. https://vcai.mpi-inf.mpg.de/projects/Deca

Articulation estimation and real-time tracking of human hand motions

Schröder M. Articulation estimation and real-time tracking of human hand motions. Bielefeld: Universität Bielefeld; 2015.This thesis deals with the problem of estimating and tracking the full articulation of human hands. Algorithmically recovering hand articulations is a challenging problem due to the hand’s high number of degrees of freedom and the complexity of its motions. Besides the accuracy and efficiency of the hand posture estimation, hand tracking methods are faced with issues such as invasiveness, ease of deployment and sensor artifacts. In this thesis several different hand tracking approaches are examined, including marker-based optical motion capture, data-driven discriminative visual tracking and generative tracking based on articulated registration, and various contributions to these areas are presented. The problem of optimally placing reduced marker sets on a performer’s hand for optical hand motion capture is explored. A method is proposed that automatically generates functional reduced marker layouts by optimizing for their numerical stability and geometric feasibility. A data-driven discriminative tracking approach based on matching the hand’s appearance in the sensor data with an image database is investigated. In addition to an efficient nearest neighbor search for images, a combination of discriminative initialization and generative refinement is employed. The method’s applicability is demonstrated in interactive robot teleoperation. Various real human hand motions are captured and statistically analyzed to derive low-dimensional representations of hand articulations. An adaptive hand posture subspace concept is developed and integrated into a generative real-time hand tracking approach that aligns a virtual hand model with sensor point clouds based on constrained inverse kinematics. Generative hand tracking is formulated as a regularized articulated registration process, in which geometrical model fitting is combined with statistical, kinematic and temporal regularization priors. A registration concept that combines 2D and 3D alignment and explicitly accounts for occlusions and visibility constraints is devised. High-quality, non-invasive, real-time hand tracking is achieved based on this regularized articulated registration formulation

Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object’s shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning e↵ectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow

Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model

Real-time marker-less hand tracking is of increasing importance in human-computer interaction. Robust and accurate tracking of arbitrary hand motion is a challenging problem due to the many degrees of freedom, frequent self-occlusions, fast motions, and uniform skin color. In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time. The main contributions include a new generative tracking method which employs an implicit hand shape representation based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is smooth and analytically differentiable making fast gradient based pose optimization possible. This shape representation, together with a full perspective projection model, enables more accurate hand modeling than a related baseline method from literature. Our method achieves better accuracy than previous methods and runs at 25 fps. We show these improvements both qualitatively and quantitatively on publicly available datasets.Comment: 8 pages, Accepted version of paper published at 3DV 201

Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.Comment: Accepted for publication by the International Journal of Computer Vision (IJCV) on 16.02.2016 (submitted on 17.10.14). A combination into a single framework of an ECCV'12 multicamera-RGB and a monocular-RGBD GCPR'14 hand tracking paper with several extensions, additional experiments and detail

Real-time 3D Tracking of Articulated Tools for Robotic Surgery

In robotic surgery, tool tracking is important for providing safe tool-tissue interaction and facilitating surgical skills assessment. Despite recent advances in tool tracking, existing approaches are faced with major difficulties in real-time tracking of articulated tools. Most algorithms are tailored for offline processing with pre-recorded videos. In this paper, we propose a real-time 3D tracking method for articulated tools in robotic surgery. The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matching and 3D pose estimation. A robust verification approach is incorporated to reject outliers in 2D detections, which is then followed by fusing inliers with robot kinematic readings for 3D pose estimation of the tool. The proposed method has been validated with phantom data, as well as ex vivo and in vivo experiments. The results derived clearly demonstrate the performance advantage of the proposed method when compared to the state-of-the-art.Comment: This paper was presented in MICCAI 2016 conference, and a DOI was linked to the publisher's versio