This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.

This paper addresses the problem of human pose estimation, given images taken from multiple dynamic but calibrated cameras. We consider solving this task using a part-based model and focus on the part appearance component of such a model. We use a random forest classifier to capture the variation in appearance of body parts in 2D images. The result of these 2D part detectors are then aggregated across views to produce consistent 3D hypotheses for parts. We solve correspondences across views for mirror symmetric parts by introducing a latent variable. We evaluate our part detectors qualitatively and quantitatively on a dataset gathered from a professional football game.

P a r t Cl a s s i f i c a t i o n I n i t i a l Co r r e s p o n d e n c e s F i n a l Co r r e s p o n d e n c e s Re c o n s t r u c t e d Mo d e l Re n d e r e d wi t h Un wr a p p e d T e x t u r e Re t e x t u r e d Re n d e r e d wi t h Un wr a p p e d T e x t u r e Re c o n s t r u c t e d mo d e l o v e r i n p u t d e p t h ma p I n p u t De p t h Ma pFigure 1: Our method starts with estimating dense correspondences on an input depth image, using a discriminative model. A generative model parametrized by blend shapes is then utilized to further refine these correspondences. The final correspondence field is used for per-frame 3D face shape and expression reconstruction, allowing for texture unwrapping, retexturing or retargeting in real-time. AbstractThis paper contributes a real time method for recovering facial shape and expression from a single depth image. The method also estimates an accurate and dense correspondence field between the input depth image and a generic face model. Both outputs are a result of minimizing the error in reconstructing the depth image, achieved by applying a set of identity and expression blend shapes to the model. Traditionally, such a generative approach has shown to be computationally expensive and non-robust because of the non-linear nature of the reconstruction error. To overcome this problem, we use a discriminatively trained prediction pipeline that employs random forests to generate an initial dense but noisy correspondence field. Our method then exploits a fast ICP-like approximation to update these correspondences, allowing us to quickly obtain a robust initial fit of our model. The model parameters are then fine tuned to minimize the true reconstruction error using a stochastic optimization technique. The correspondence field resulting from our hybrid generative-discriminative pipeline is accurate and useful for a variety of applications such as mesh deformation and retexturing. Our method works in real-time on a single depth image i.e. without temporal tracking, is free from per-user calibration, and works in low-light conditions.

We present a fully automatic procedure for reconstructing the pose of a person in 3D from images taken from multiple views. We demonstrate a novel approach for learning more complex models using SVM-Rank, to reorder a set of high scoring configurations. The new model in many cases can resolve the problem of double counting of limbs which happens often in the pictorial structure based models. We address the problem of flipping ambiguity to find the correct correspondences of 2D predictions across all views. We obtain improvements for 2D prediction over the state of art methods on our dataset. We show that the results in many cases are good enough for a fully automatic 3D reconstruction with uncalibrated cameras.

We propose a new method for face alignment with part-based modeling. This method is competitive in terms of precision with existing methods such as Active Appearance Models, but is more robust and has a superior generalization ability due to its part-based nature. A variation of the Histogram of Oriented Gradients descriptor is used to model the appearance of each part and the shape information is represented with a set of landmark points around the major facial features. Multiple linear regression models are learnt to estimate the position of the landmarks from the appearance of each part. We verify our algorithm with a set of experiments on human faces and these show the competitive performance of our method compared to existing methods.

Introduction Coronavirus disease 2019 (COVID-19) is a devastating pandemic that may also affect the nervous system. One of its neurological manifestations is intracerebral hemorrhage (ICH). Data about pure spontaneous intraparenchymal hemorrhage related to COVID-19 is scarce. In this study, we present some patients with COVID-19 disease who also had spontaneous intraparenchymal hemorrhage along with a review of the literature. Methods This single-center prospective study was done among 2,862 patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) between March 1 and November 1, 2020. Out of 2,862 patients with SARS-CoV-2, 14 patients with neurological manifestations were assessed with a noncontrast brain computed tomography scan. Seven patients with spontaneous intraparenchymal hemorrhage were enrolled. Results All seven patients were male, with a mean age of 60.8 years old. Six patients (85.7%) only had minimal symptoms of COVID-19 without significant respiratory distress. The level of consciousness in two patients (28.5%) was less than eight, according to the Glasgow Coma Scale (GCS). Hypertension (71.4%) was the most common risk factor in their past medical history. The mean volume of hematoma was 41cc. Four patients died during hospitalization, and the others were discharged with a mean hospital stay of 42.6 days. All patients with GCS less than 11 died. Conclusion It concluded that ICH patients with COVID-19 are related to higher blood volume, cortical and subcortical location of hemorrhage, higher fatality rate, and younger age that is different to spontaneous ICH in general population. We recommend more specific neuroimaging in patients with COVID 19 such as brain magnetic resonance imaging concomitant with vascular studies in future. The impact of COVID-19 on mortality rate is not clear because of limited epidemiologic studies, but identifying the causal relationship between COVID-19 and ICH requires further clinical and laboratory studies.