xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera

We present a new solution to egocentric 3D body pose estimation from monocular images captured from a downward looking fish-eye camera installed on the rim of a head mounted virtual reality device. This unusual viewpoint, just 2 cm away from the user's face, leads to images with unique visual appearance, characterized by severe self-occlusions and strong perspective distortions that result in a drastic difference in resolution between lower and upper body. Our contribution is two-fold. Firstly, we propose a new encoder-decoder architecture with a novel dual branch decoder designed specifically to account for the varying uncertainty in the 2D joint locations. Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches. Our second contribution is a new large-scale photorealistic synthetic dataset - xR-EgoPose - offering 383K frames of high quality renderings ofpeople with a diversity of skin tones, body shapes, clothing, in a variety of backgrounds and lighting conditions, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of the art results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint

Adult-Acquired Flatfoot Deformity and Age-Related Differences in Foot and Ankle Kinematics During the Single-Limb Heel-Rise Test

STUDY DESIGN: Cross-sectional laboratory study. OBJECTIVE: To compare single-limb heel-rise performance and foot-ankle kinematics between persons with stage 2 adult-acquired flatfoot deformity (AAFD) and healthy controls. BACKGROUND: The inability to perform a single-limb heel rise is considered a positive functional diagnostic test for AAFD. However, which foot motions contribute to poor performance of this task are not known. METHODS: Fifty individuals participated in this study, 20 with stage 2 AAFD (mean +/- SD age, 57.6 +/- 11.3 years), and 15 older participants (age, 56.8 +/- 5.3 years) and 15 younger participants (age, 22.2 +/- 2.4 years) without AAFD as control groups. Forefoot (sagittal plane) and rearfoot (sagittal and frontal planes) kinematics were collected using a 3-D motion analysis system. Heel-rise performance (heel height) and kinematics (joint angles, excursions) were evaluated. One-way and 2-way analyses of variance were used to examine differences in heel-rise performance and kinematics between groups. RESULTS: Individuals with AAFD and older controls demonstrated lower heel-rise height than those in the younger control group (P\u3c.001). Persons with AAFD demonstrated higher degrees of first metatarsal dorsiflexion (P\u3c.001), lower ankle plantar flexion (P\u3c.001), and higher subtalar eversion (P = .027) than those in the older control group. Persons with AAFD demonstrated lower ankle excursion (P\u3c.001) and first metatarsal excursion (P\u3c.001) than those in the older control group, but no difference in subtalar excursion (P = .771). CONCLUSION: Persons with stage 2 AAFD did not achieve sufficient heel height during a single-leg heel rise. Both forefoot and rearfoot kinematics in the sagittal plane, as opposed to the frontal plane, contributed to the lower heel height in participants with stage 2 AAFD. Older controls demonstrated lower heel-rise height than younger controls, indicating that clinical expectations of heel-rise performance may need to be adjusted for age

Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state-of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors

{SelfPose}: {3D} Egocentric Pose Estimation from a Headset Mounted Camera

We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches. To tackle the lack of labelled data we also introduced a large photo-realistic synthetic dataset. xR-EgoPose offers high quality renderings of people with diverse skintones, body shapes and clothing, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of theart results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.Comment: 14 pages. arXiv admin note: substantial text overlap with arXiv:1907.1004

Trabectedin arrests a doxorubicin-resistant PDGFRA-activated liposarcoma patient-derived orthotopic xenograft (PDOX) nude mouse model.

BACKGROUND:Pleomorphic liposarcoma (PLPS) is a rare, heterogeneous and an aggressive variant of liposarcoma. Therefore, individualized therapy is urgently needed. Our recent reports suggest that trabectedin (TRAB) is effective against several patient-derived orthotopic xenograft (PDOX) mouse models. Here, we compared the efficacy of first-line therapy, doxorubicin (DOX), and TRAB in a platelet-derived growth factor receptor-α (PDGFRA)-amplified PLPS. METHODS:We used a fresh sample of PLPS tumor derived from a 68-year-old male patient diagnosed with a recurrent PLPS. Subcutaneous implantation of tumor tissue was performed in a nude mouse. After three weeks of implantation, tumor tissues were isolated and cut into small pieces. To match the patient a PDGFRA-amplified PLPS PDOX was created in the biceps femoris of nude mice. Mice were randomized into three groups: Group 1 (G1), control (untreated); Group 2 (G2), DOX-treated; Group 3 (G3), TRAB-treated. Measurement was done twice a week for tumor width, length, and mouse body weight. RESULTS:The PLPS PDOX showed resistance towards DOX. However, TRAB could arrest the PLPS (p < 0.05 compared to control; p < 0.05 compared to DOX) without any significant changes in body-weight. CONCLUSIONS:The data presented here suggest that for the individual patient the PLPS PDOX model could specifically distinguish both effective and ineffective drugs. This is especially crucial for PLPS because effective first-line therapy is harder to establish if it is not individualized

Average consensus and gossip algorithms in networks with stochastic asymmetric communications

We consider that a set of distributed agents desire to reach consensus on the average of their initial state values, while communicating with neighboring agents through a shared medium. This communication medium allows only one agent to transmit unidirectionally at a given time, which is true, e.g., in wireless networks. We address scenarios where the choice of agents that transmit and receive messages at each transmission time follows a stochastic characterization, and we model the topology of allowable transmissions with asymmetric graphs. In particular, we consider: (i) randomized gossip algorithms in wireless networks, where each agent becomes active at randomly chosen times, transmitting its data to a single neighbor; (ii) broadcast wireless networks, where each agent transmits to all the other agents, and access to the network occurs with the same probability for every node. We propose a solution in terms of a linear distributed algorithm based on a state augmentation technique, and prove that this solution achieves average consensus in a stochastic sense, for the special cases (i) and (ii). Expressions for absolute time convergence rates at which average consensus is achieved are also given