I am working on leveraging machine learning techniques for better inference in computer vision applications. The main research question is how to make use of learning techniques such as deep neural networks and random forests for inference in structured prediction frameworks.
Drew is a PhD candidate (2012-present) in Neuroscience at the University of Pennsylvania and a visiting researcher in the Perceiving Systems department of the Max Planck Institute for Intelligent Systems in Tübingen (2015). His research is focused on uncovering the computational principles underlying the organization of networks of cortical neurons to develop general approaches to solving computer vision problems. At Penn, he is investigating computational approaches to understanding visual motion processing under the supervision of Diego Contreras and Kostas Daniilidis.
PS Web Developer
My research is devoted to the design of better algorithms for solving computer vision problems as well as their applications in related fields such as computer graphics, medical image analysis and robotics. I have been focusing on the advances of graphical models and the development of graph-based approaches for fundamental computer vision problems, such as segmentation, tracking, shape matching and 3D model inference.
I am interested in recovering physical properties of general scenes from images. My current research focuses on reconstructing dense 3-d surface geometry, appearance and motion of dynamic scenes from multi-view image sequences. I believe accurate estimation of these properties will help understand the physical world.
My research is related to matching non-rigid articulated objects with emphasis on 3D models of the human body.
Designing, draping, and animating virtual clothing on realistic body shapes in motion. Human subjects coordination. Human 3D, 4D and motion capture.
My research focuses on human body modeling and 3D mesh registration. In particular, I work on the development of registration algorithms that exploit both 3D shape and texture information. I am also interested in segmentation and registration of medical (namely, dermatologic) images.
I'm the department's friendly neighbourhood Sysadmin. 10000ft tall and made of string.
I am interested in efficient inference methods for computer vision. What makes models stand out to allow fast inference and how push the computational burden towards training time? In particular, I am working on human pose estimation from single images, a challenging structured prediction problem.
I'm working at the intersection of machine learning and computer vision, developing efficient non-parametric models of human pose, motion, appearance, and segmentation. I'm especially interested in exploiting the additional information provided by the dynamic context present in temporal data sources.
My work focuses on improving human body shape and pose estimation from different sensor based systems. I help develop tools and methods for capturing and processing data from depth/ToF sensors and mocap-like systems. I am also involved in developing tools to implement research projects into existing computer graphics and animation pipelines.
My research focuses on the computational analysis of video sequences: In what ways can the temporal dimension of videos be used by a computer to better understand the structure of a scene? And what can we learn from dynamic stimuli processing in the human visual system to make our algorithms more robust?
My research interest focuses on the use of probabilistic methods in the modeling and representation of shapes and images, and the analysis of biomedical models.
I am interested in modeling nonrigid objects. The geometric relations between a camera and the rigid world are well-known in Multiview Geometry, whereas little is understood about nonrigid objects. Nonrigid geometric concepts can have a huge impact in computer vision in terms of video understanding and in computer graphics in terms of rendering dynamic objects. In particular, I worked on nonrigid structure from motion, representation of nonrigid structures, and 3D human pose estimation.
During my studies in Biology I worked on the relationship between body shape and attractiveness perception. In our department I am responsible for coordinating research trials to collect data on human body shape and pose including 3D body scans and anthropometric measurements. The overall aim is to generate data for the body model as well as other research or art projects.
I am a research group leader at the Bernstein Center for Computational Neuroscience and the Max Planck Instiute for Intelligent Systems in Tübingen. Before, I was postdoctoral researcher at ETH Zurich, temporary Professor at TU Darmstadt, and Junior Research Group Leader at the MPI for Informatik. I did my PhD studies in the Empirical Inference group at the MPI for Biological Cybernetics. My main research focus lies on visual scene understanding. I want to enable computers to reason about the physical world around us. This requires models that can infer about the semantic, as well as the physical properties of visual data. I believe that both problems should be addressed jointly. I have broad interest in computer vision and machine learning topics with a focus on statistical models and inference techniques. Applications range from material and reflectance separation, to detection and pose estimation.
My research focuses on how intelligent systems can understand a visual world that is constantly changing. Currently my main interest is how to recover fundamental scene properties (i.e. intrinsic images) registered in the pixel grid of the video sequences, such as albedo, shading, optical flow, surface boundaries, and depth.
I am interested in Computer Vision and Machine Learning with a focus on temporal analysis, action detection and facial expression analysis. I am also interested in Compressed Sensing and rank minimization for temporal feature representation.
I am interested in computer vision and machine learning. I am currently working on using neural networks for semantic segmentation and 2D keypoint detection for pose estimation on animal images.
I am a first year PhD student at the Computer Vision and Active Perception Lab, CSC, at KTH. My research focuses on human-human and human-robot interaction and computational models thereof. From mid April to mid Juli 2016 I am doing an internship at the Perceiving Systems Department.
I am interested in model-based 3D reconstruction of animals!
I'm an intern for January 2018 working on relating speech and facial movement.
Research on Active Cooperative SLAM
My research interests are in capturing, modeling and understanding human face / body.
Phd student from Georgia Tech, working on 3D scene flow and motion estimation.
I am a doctoral student in the Department of Electrical Engineering at the Indian Institute of Science - Bangalore, advising by Dr Soma Biswas, Assistant Professor. My research interests are in computer vision and pattern recognition with applications to low resolution face recognition.
I work with the camera scanners available in the PS department. I provide support for the researchers, together with managing and upgrading the scanning systems.
I am an intern for Summer 2017, working on optical flow algorithm on mobile and embedded systems. I am currently a masters student in the School of Computing, Informatics and Design System Engineering (CIDSE) at Arizona State University, USA. My research interests are in computer vision, graphics and mobile systems.
I am doing my bachelor thesis as part of an internship. My work focuses on Shape Characterization and 3D Localization of Internal Organs from Medical Images.
I am currently writing my B.A. thesis, which is a continuation of my summer internship at the PS department and focuses on applying my computational linguistics knowledge to identify physical descriptions from text. My goal is to be able to extract and label body features from a text using word embeddings and syntax and semantics analysis tools.