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

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

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

Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body is a time consuming and labour intensive postprocessing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework

Monocular Expressive Body Regression through Body-Driven Attention

To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de .Comment: Accepted in ECCV'20. Project page: http://expose.is.tue.mpg.d

Floral advertisement scent in a changing plant-pollinators market

Plant-pollinator systems may be considered as biological markets in which pollinators choose between different flowers that advertise their nectar/pollen rewards. Although expected to play a major role in structuring plant-pollinator interactions, community-wide patterns of flower scent signals remain largely unexplored. Here we show for the first time that scent advertisement is higher in plant species that bloom early in the flowering period when pollinators are scarce relative to flowers than in species blooming later in the season when there is a surplus of pollinators relative to flowers. We also show that less abundant flowering species that may compete with dominant species for pollinator visitation early in the flowering period emit much higher proportions of the generalist attractant β-ocimene. Overall, we provide a first community-wide description of the key role of seasonal dynamics of plant-specific flower scent emissions, and reveal the coexistence of contrasting plant signaling strategies in a plant-pollinator market

Model-free Consensus Maximization for Non-Rigid Shapes

Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using `rules' on measurements. We then provide a method to solve it optimally using the Branch and Bound (BnB) paradigm. We focus its application on non-rigid shapes, where we apply the method to remove outlier 3D correspondences and achieve performance superior to the state of the art. Our method works with outlier ratio as high as 80\%. We further derive a similar formulation for 3D template to image matching, achieving similar or better performance compared to the state of the art.Comment: ECCV1

A modeling and simulation study of siderophore mediated antagonism in dual-species biofilms

<p>Abstract</p> <p>Background</p> <p>Several bacterial species possess chelation mechanisms that allow them to scavenge iron from the environment under conditions of limitation. To this end they produce siderophores that bind the iron and make it available to the cells later on, while rendering it unavailable to other organisms. The phenomenon of siderophore mediated antagonism has been studied to some extent for suspended populations where it was found that the chelation ability provides a growth advantage over species that do not have this possibility. However, most bacteria live in biofilm communities. In particular <it>Pseudomonas fluorescens </it>and <it>Pseudomonas putida</it>, the species that have been used in most experimental studies of the phenomenon, are known to be prolific biofilm formers, but only very few experimental studies of iron chelation have been published to date for the biofilm setting. We address this question in the present study.</p> <p>Methods</p> <p>Based on a previously introduced model of iron chelation and an existing model of biofilm growth we formulate a model for iron chelation and competition in dual species biofilms. This leads to a highly nonlinear system of partial differential equations which is studied in computer simulation experiments.</p> <p>Conclusions</p> <p>(i) Siderophore production can give a growth advantage also in the biofilm setting, (ii) diffusion facilitates and emphasizes this growth advantage, (iii) the magnitude of the growth advantage can also depend on the initial inoculation of the substratum, (iv) a new mass transfer boundary condition was derived that allows to a priori control the expect the expected average thickness of the biofilm in terms of the model parameters.</p