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 Human Motion Capture with Multiple Depth Cameras

Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. We also introduce a dataset of ~6 million synthetic depth frames for pose estimation from multiple cameras and exceed state-of-the-art results on the Berkeley MHAD dataset.Comment: Accepted to computer robot vision 201

Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies

We present a unified deformation model for the markerless capture of multiple scales of human movement, including facial expressions, body motion, and hand gestures. An initial model is generated by locally stitching together models of the individual parts of the human body, which we refer to as the "Frankenstein" model. This model enables the full expression of part movements, including face and hands by a single seamless model. Using a large-scale capture of people wearing everyday clothes, we optimize the Frankenstein model to create "Adam". Adam is a calibrated model that shares the same skeleton hierarchy as the initial model but can express hair and clothing geometry, making it directly usable for fitting people as they normally appear in everyday life. Finally, we demonstrate the use of these models for total motion tracking, simultaneously capturing the large-scale body movements and the subtle face and hand motion of a social group of people

Multiview 3D markerless human pose estimation from OpenPose skeletons

Despite the fact that marker-based systems for human motion estimation provide very accurate tracking of the human body joints (at mm precision), these systems are often intrusive or even impossible to use depending on the circumstances, e.g.~markers cannot be put on an athlete during competition. Instrumenting an athlete with the appropriate number of markers requires a lot of time and these markers may fall off during the analysis, which leads to incomplete data and requires new data capturing sessions and hence a waste of time and effort. Therefore, we present a novel multiview video-based markerless system that uses 2D joint detections per view (from OpenPose) to estimate their corresponding 3D positions while tackling the people association problem in the process to allow the tracking of multiple persons at the same time. Our proposed system can perform the tracking in real-time at 20-25 fps. Our results show a standard deviation between 9.6 and 23.7 mm for the lower body joints based on the raw measurements only. After filtering the data, the standard deviation drops to a range between 6.6 and 21.3 mm. Our proposed solution can be applied to a large number of applications, ranging from sports analysis to virtual classrooms where submillimeter precision is not necessarily required, but where the use of markers is impractical

06241 Abstracts Collection -- Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop

From 11.06.06 to 16.06.06, the Dagstuhl Seminar 06241 ``Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop "Theoretical Foundations of Computer Vision"\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general