Human motion reconstruction using wearable accelerometers

We address the problem of capturing human motion in scenarios where the use of a traditional optical motion capture system is impractical. Such scenarios are relatively commonplace, such as in large spaces, outdoors or at competitive sporting events, where the limitations of such systems are apparent: the small physical area where motion capture can be done and the lack of robustness to lighting changes and occlusions. In this paper, we advocate the use of body-worn wearable wireless accelerometers for reconstructing human motion and to this end we outline a system that is more portable than traditional optical motion capture systems, whilst producing naturalistic motion. Additionally, if information on the person's root position is available, an extended version of our algorithm can use this information to correct positional drift

MonoPerfCap: Human Performance Capture from Monocular Video

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201

Non-Rigid Structure from Motion for Complex Motion

Recovering deformable 3D motion from temporal 2D point tracks in a monocular video is an open problem with many everyday applications throughout science and industry, or the new augmented reality. Recently, several techniques have been proposed to deal the problem called Non-Rigid Structure from Motion (NRSfM), however, they can exhibit poor reconstruction performance on complex motion. In this project, we will analyze these situations for primitive human actions such as walk, run, sit, jump, etc. on different scenarios, reviewing first the current techniques to finally present our novel method. This approach is able to model complex motion into a union of subspaces, rather than the summation occurring in standard low-rank shape methods, allowing better reconstruction accuracy. Experiments in a wide range of sequences and types of motion illustrate the benefits of this new approac

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

Subsecond total-body imaging using ultrasensitive positron emission tomography.

A 194-cm-long total-body positron emission tomography/computed tomography (PET/CT) scanner (uEXPLORER), has been constructed to offer a transformative platform for human radiotracer imaging in clinical research and healthcare. Its total-body coverage and exceptional sensitivity provide opportunities for innovative studies of physiology, biochemistry, and pharmacology. The objective of this study is to develop a method to perform ultrahigh (100 ms) temporal resolution dynamic PET imaging by combining advanced dynamic image reconstruction paradigms with the uEXPLORER scanner. We aim to capture the fast dynamics of initial radiotracer distribution, as well as cardiac motion, in the human body. The results show that we can visualize radiotracer transport in the body on timescales of 100 ms and obtain motion-frozen images with superior image quality compared to conventional methods. The proposed method has applications in studying fast tracer dynamics, such as blood flow and the dynamic response to neural modulation, as well as performing real-time motion tracking (e.g., cardiac and respiratory motion, and gross body motion) without any external monitoring device (e.g., electrocardiogram, breathing belt, or optical trackers)