8,054 research outputs found
Robust Hand Motion Capture and Physics-Based Control for Grasping in Real Time
Hand motion capture technologies are being explored due to high demands in the fields such as video game, virtual reality, sign language recognition, human-computer interaction, and robotics. However, existing systems suffer a few limitations, e.g. they are high-cost (expensive capture devices), intrusive (additional wear-on sensors or complex configurations), and restrictive (limited motion varieties and restricted capture space). This dissertation mainly focus on exploring algorithms and applications for the hand motion capture system that is low-cost, non-intrusive, low-restriction, high-accuracy, and robust.
More specifically, we develop a realtime and fully-automatic hand tracking system using a low-cost depth camera. We first introduce an efficient shape-indexed cascaded pose regressor that directly estimates 3D hand poses from depth images. A unique property of our hand pose regressor is to utilize a low-dimensional parametric hand geometric model to learn 3D shape-indexed features robust to variations in hand shapes, viewpoints and hand poses. We further introduce a hybrid tracking scheme that effectively complements our hand pose regressor with model-based hand tracking. In addition, we develop a rapid 3D hand shape modeling method that uses a small number of depth images to accurately construct a subject-specific skinned mesh model for hand tracking. This step not only automates the whole tracking system but also improves the robustness and accuracy of model-based tracking and hand pose regression.
Additionally, we also propose a physically realistic human grasping synthesis method that is capable to grasp a wide variety of objects. Given an object to be grasped, our method is capable to compute required controls (e.g. forces and torques) that advance the simulation to achieve realistic grasping. Our method combines the power of data-driven synthesis and physics-based grasping control. We first introduce a data-driven method to synthesize a realistic grasping motion from large sets of prerecorded grasping motion data. And then we transform the synthesized kinematic motion to a physically realistic one by utilizing our online physics-based motion control method. In addition, we also provide a performance interface which allows the user to act out before a depth camera to control a virtual object
Robust Hand Motion Capture and Physics-Based Control for Grasping in Real Time
Hand motion capture technologies are being explored due to high demands in the fields such as video game, virtual reality, sign language recognition, human-computer interaction, and robotics. However, existing systems suffer a few limitations, e.g. they are high-cost (expensive capture devices), intrusive (additional wear-on sensors or complex configurations), and restrictive (limited motion varieties and restricted capture space). This dissertation mainly focus on exploring algorithms and applications for the hand motion capture system that is low-cost, non-intrusive, low-restriction, high-accuracy, and robust.
More specifically, we develop a realtime and fully-automatic hand tracking system using a low-cost depth camera. We first introduce an efficient shape-indexed cascaded pose regressor that directly estimates 3D hand poses from depth images. A unique property of our hand pose regressor is to utilize a low-dimensional parametric hand geometric model to learn 3D shape-indexed features robust to variations in hand shapes, viewpoints and hand poses. We further introduce a hybrid tracking scheme that effectively complements our hand pose regressor with model-based hand tracking. In addition, we develop a rapid 3D hand shape modeling method that uses a small number of depth images to accurately construct a subject-specific skinned mesh model for hand tracking. This step not only automates the whole tracking system but also improves the robustness and accuracy of model-based tracking and hand pose regression.
Additionally, we also propose a physically realistic human grasping synthesis method that is capable to grasp a wide variety of objects. Given an object to be grasped, our method is capable to compute required controls (e.g. forces and torques) that advance the simulation to achieve realistic grasping. Our method combines the power of data-driven synthesis and physics-based grasping control. We first introduce a data-driven method to synthesize a realistic grasping motion from large sets of prerecorded grasping motion data. And then we transform the synthesized kinematic motion to a physically realistic one by utilizing our online physics-based motion control method. In addition, we also provide a performance interface which allows the user to act out before a depth camera to control a virtual object
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
We present an approach that uses a multi-camera system to train fine-grained
detectors for keypoints that are prone to occlusion, such as the joints of a
hand. We call this procedure multiview bootstrapping: first, an initial
keypoint detector is used to produce noisy labels in multiple views of the
hand. The noisy detections are then triangulated in 3D using multiview geometry
or marked as outliers. Finally, the reprojected triangulations are used as new
labeled training data to improve the detector. We repeat this process,
generating more labeled data in each iteration. We derive a result analytically
relating the minimum number of views to achieve target true and false positive
rates for a given detector. The method is used to train a hand keypoint
detector for single images. The resulting keypoint detector runs in realtime on
RGB images and has accuracy comparable to methods that use depth sensors. The
single view detector, triangulated over multiple views, enables 3D markerless
hand motion capture with complex object interactions.Comment: CVPR 201
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
Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation
Accurate and robust object state estimation enables successful object
manipulation. Visual sensing is widely used to estimate object poses. However,
in a cluttered scene or in a tight workspace, the robot's end-effector often
occludes the object from the visual sensor. The robot then loses visual
feedback and must fall back on open-loop execution.
In this paper, we integrate both tactile and visual input using a framework
for solving the SLAM problem, incremental smoothing and mapping (iSAM), to
provide a fast and flexible solution. Visual sensing provides global pose
information but is noisy in general, whereas contact sensing is local, but its
measurements are more accurate relative to the end-effector. By combining them,
we aim to exploit their advantages and overcome their limitations. We explore
the technique in the context of a pusher-slider system. We adapt iSAM's
measurement cost and motion cost to the pushing scenario, and use an
instrumented setup to evaluate the estimation quality with different object
shapes, on different surface materials, and under different contact modes
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
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