Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences

We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting. Our approach relies on geometric features (edges and landmarks) and, inspired by the iterated closest point algorithm, is based on computing hard correspondences between model vertices and edge pixels. We demonstrate that this is superior to previous work that uses soft correspondences to form an edge-derived cost surface that is minimised by nonlinear optimisation.Comment: To appear in ACCV 2016 Workshop on Facial Informatic

The Toxoplasma gondii active serine hydrolase 4 regulates parasite division and intravacuolar parasite architecture

ABSTRACT Hydrolase are enzymes that regulate diverse biological processes, including posttranslational protein modifications. Recent work identified four active serine hydrolases (ASHs) in Toxoplasma gondii as candidate depalmitoylases. However, only TgPPT1 (ASH1) has been confirmed to remove palmitate from proteins. ASH4 (TgME49_264290) was reported to be refractory to genetic disruption. We demonstrate that recombinant ASH4 is an esterase that processes short acyl esters but not palmitoyl thioesters. Genetic disruption of ASH4 causes defects in cell division and premature scission of parasites from residual bodies. These defects lead to the presence of vacuoles with a disordered intravacuolar architecture, with parasites arranged in pairs around multiple residual bodies. Importantly, we found that the deletion of ASH4 correlates with a defect in radial dispersion from host cells after egress. This defect in dispersion of parasites is a general phenomenon that is observed for disordered vacuoles that occur at low frequency in wild-type parasites, suggesting a possible general link between intravacuolar organization and dispersion after egress. IMPORTANCE This work defines the function of an enzyme in the obligate intracellular parasite Toxoplasma gondii. We show that this previously uncharacterized enzyme is critical for aspects of cellular division by the parasite and that loss of this enzyme leads to parasites with cell division defects and which also are disorganized inside their vacuoles. This leads to defects in the ability of the parasite to disseminate from the site of an infection and may have a significant impact on the parasite's overall infectivity of a host organism

Explaining the US Bond Yield Conundrum

We analyze if and to what extent fundamental macroeconomic factors, temporary influences or more structural factors have contributed to the low levels of US bond yields over the last few years. For that purpose, we start with a general model of interest rate determination. The empirical part consists of a cointegration analysis with an error correction mechanism. We are able to establish a stable long-run relationship and find that the behavior of bond yields, even during the last two years, can well be explained. Alongside the more traditional macroeconomic determinants like core inflation, monetary policy and the business cycle, we also include foreign holdings of US Treasuries. The latter should capture the frequently mentioned structural effects on long-term interest rates. Finally, our bond yield equation outperforms a random walk model in different forecasting exercises.bond yields; interest rates; cointegration; inflation; forecasting

Thoughts on Eggert's Conjecture

Eggert's Conjecture says that if R is a finite-dimensional nilpotent commutative algebra over a perfect field F of characteristic p, and R^{(p)} is the image of the p-th power map on R, then dim_F R \geq p dim_F R^{(p)}. Whether this very elementary statement is true is not known. We examine heuristic evidence for this conjecture, versions of the conjecture that are not limited to positive characteristic and/or to commutative R, consequences the conjecture would have for semigroups, and examples that give equality in the conjectured inequality. We pose several related questions, and briefly survey the literature on the subject.Comment: 12 pages. Copy at http://math.berkeley.edu/~gbergman/papers may be updated more frequently than arXiv copy. A few misstatements in the first version have been corrected, and the wording improved in place

Inhibitors of SARS-CoV entry--identification using an internally-controlled dual envelope pseudovirion assay.

Severe acute respiratory syndrome-associated coronavirus (SARS-CoV) emerged as the causal agent of an endemic atypical pneumonia, infecting thousands of people worldwide. Although a number of promising potential vaccines and therapeutic agents for SARS-CoV have been described, no effective antiviral drug against SARS-CoV is currently available. The intricate, sequential nature of the viral entry process provides multiple valid targets for drug development. Here, we describe a rapid and safe cell-based high-throughput screening system, dual envelope pseudovirion (DEP) assay, for specifically screening inhibitors of viral entry. The assay system employs a novel dual envelope strategy, using lentiviral pseudovirions as targets whose entry is driven by the SARS-CoV Spike glycoprotein. A second, unrelated viral envelope is used as an internal control to reduce the number of false positives. As an example of the power of this assay a class of inhibitors is reported with the potential to inhibit SARS-CoV at two steps of the replication cycle, viral entry and particle assembly. This assay system can be easily adapted to screen entry inhibitors against other viruses with the careful selection of matching partner virus envelopes

Morphable Face Models - An Open Framework

In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model

Generating 3D faces using Convolutional Mesh Autoencoders

Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://github.com/anuragranj/com