CLOTH3D: Clothed 3D Humans

This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape

Real-time gestural control of robot manipulator through Deep Learning human-pose inference

International audienceWith the raise of collaborative robots, human-robot interaction needs to be as natural as possible. In this work, we present a framework for real-time continuous motion control of a real collabora-tive robot (cobot) from gestures captured by an RGB camera. Through deep learning existing techniques, we obtain human skeletal pose information both in 2D and 3D. We use it to design a controller that makes the robot mirror in real-time the movements of a human arm or hand

Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.Comment: To appear in ECCV 201

Inner Space Preserving Generative Pose Machine

Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image "inner space" preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.Comment: http://www.northeastern.edu/ostadabbas/2018/07/23/inner-space-preserving-generative-pose-machine

Radiography of the Earth's Core and Mantle with Atmospheric Neutrinos

A measurement of the absorption of neutrinos with energies in excess of 10 TeV when traversing the Earth is capable of revealing its density distribution. Unfortunately, the existence of beams with sufficient luminosity for the task has been ruled out by the AMANDA South Pole neutrino telescope. In this letter we point out that, with the advent of second-generation kilometer-scale neutrino detectors, the idea of studying the internal structure of the Earth may be revived using atmospheric neutrinos instead.Comment: 4 pages, LaTeX file using RevTEX4, 2 figures and 1 table included. Matches published versio

Some closure operations in Zariski-Riemann spaces of valuation domains: a survey

In this survey we present several results concerning various topologies that were introduced in recent years on spaces of valuation domains

Why dynamos are prone to reversals

In a recent paper (Phys. Rev. Lett. 94 (2005), 184506; physics/0411050) it was shown that a simple mean-field dynamo model with a spherically symmetric helical turbulence parameter alpha can exhibit a number of features which are typical for Earth's magnetic field reversals. In particular, the model produces asymmetric reversals, a positive correlation of field strength and interval length, and a bimodal field distribution. All these features are attributable to the magnetic field dynamics in the vicinity of an exceptional point of the spectrum of the non-selfadjoint dynamo operator. The negative slope of the growth rate curve between the nearby local maximum and the exceptional point makes the system unstable and drives it to the exceptional point and beyond into the oscillatory branch where the sign change happens. A weakness of this reversal model is the apparent necessity to fine-tune the magnetic Reynolds number and/or the radial profile of alpha. In the present paper, it is shown that this fine-tuning is not necessary in the case of higher supercriticality of the dynamo. Numerical examples and physical arguments are compiled to show that, with increasing magnetic Reynolds number, there is strong tendency for the exceptional point and the associated local maximum to move close to the zero growth rate line. Although exemplified again by the spherically symmetric alpha^2 dynamo model, the main idea of this ''self-tuning'' mechanism of saturated dynamos into a reversal-prone state seems well transferable to other dynamos. As a consequence, reversing dynamos might be much more typical and may occur much more frequently in nature than what could be expected from a purely kinematic perspective.Comment: 11 pages, 10 figure

Determination of the vacancy formation enthalpy in chromium by positron annihilation

Doppler broadening of the positron annihilation lineshape in 99.99 at. % pure chromium was measured over the temperature range 296 to 2049/sup 0/K. The chromium sample was encapsulated in sapphire owing to its high vapor pressure near melting. Saturation-like behavior of the lineshape was observed near the melting temperature (2130/sup 0/K). A two-state trapping model fit to the data yielded a vacancy formation enthalpy of 2.0 +- 0.2 eV. This result is discussed in relation to extant empirical relations for vacancy migration and self-diffusion in metals and to data from previous self-diffusion and annealing experiments in chromium. It is concluded that the observed vacancy ensemble is unlikely to be responsible for the measured self-diffusion behavior. The implications of the present results in terms of our understanding of mechanisms for self-diffusion in chromium and other refractory bcc metals are discussed

Summer 1971

Mercury - Is The Amount in Seafood Poisonous? by C.J. Gilgut (3) Comparison of Several Fertilizer Salts and Their Effect on Penn Cross Creeping Bentgrass Top Growth by Joseph Troll (5) Turf Bulletin\u27s Photo Quiz by Frederick G. Cheney (7) A Review of the Trace ELements by Mark Loper (8) Now is the Time to Cut Costs by Holman M. Griffin (12) For the Homeowner -- Thatch and its Control by Joseph Troll (14) Lawns Slow Pollution by Robert W. Schery (15) Decimate the Decibels (16) Benomyl for the Control of Fusarium Blight of \u27Merion\u27 Kentucky Bluegrass by J.M. Vargas, Jr. and Charles W. Laughlin (17) New Blueprint Emerges for Air Pollution Controls (19) Preparing Turf Area Seedbeds by Thomas G. Pardy (22) An Ecologist Talks about Pollution by Donald A. Spencer (23) Editorial--Looking Back at the Mass. Turf Conference by Frederick G. Cheney (Outside back cover

Linguistic and statistically derived features for cause of death prediction from verbal autopsy text

Automatic Text Classification (ATC) is an emerging technology with economic importance given the unprecedented growth of text data. This paper reports on work in progress to develop methods for predicting Cause of Death from Verbal Autopsy (VA) documents recommended for use in low-income countries by the World Health Organisation. VA documents contain both coded data and open narrative. The task is formulated as a Text Classification problem and explores various combinations of linguistic and statistical approaches to determine how these may improve on the standard bag-of-words approach using a dataset of over 6400 VA documents that were manually annotated with cause of death. We demonstrate that a significant improvement of prediction accuracy can be obtained through a novel combination of statistical and linguistic features derived from the VA text. The paper explores the methods by which ATC may leads to improved accuracy in Cause of Death prediction