My research is focused on understanding the fundamental computational processes underlying visual perception. We combine insights from neuroscience with statistical models, machine learning, and computer graphics to derive new computer vision algorithms that, one day, may enable machines to see.

I am interested to teach computers how to understand visual scenes. With understanding I mean two things: being able to infer the physical process that created the scene but also to understand the scene on a semantic level. I believe that these are tasks that require a principled modeling language that enables us to encode knowledge about the underlying process. To this end I study various models, currently I focus on statistical models.

I am interested in computer vision and machine learning with a focus on 3D scene understanding, parsing and reconstruction. During my Ph.D. I have developed probabilistic models for 3D traffic scene understanding from movable platforms.

One of the requirements for enabling machines to perceive and interact in a human environment is to accurately perceive humans and their activities. My research is related to different aspects of movement perception and modeling.

In the PS group I’m at least nominally responsible for anything with a plug on the end of it - whether it’s a printer, a compute server or the world’s most accurate and detailed 3D Body Scanner. Whenever I can, I act as the department's armchair design guru. I love fixing problems with interesting technology and putting big smiles on faces.

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.

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.

My interests are shape analysis and non-rigid registration of images and video, and their biomedical applications. My current research explores the temporal aspects of optical flow 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 coordinate our department's research trials to collect data on human body shape and pose including 3D body scans and anthropometric measurements. Our research trials also aim to explore health and other applications of the body model.

My research focuses on how intelligent systems can understand a visual world that is constantly changing. I explore how to identify and track stable structures in the scene over time by exploiting underlying physical properties of the scene that are invariant to motion and lighting.

I am interested in creating automated methods that improve existing graphics pipelines in the animation industry. My work involves human shape and pose estimation from various data sources, 3D scans to sparse markers, and creating realistic, articulated models that are compatible with 3D animation environments and game engines.

Are there people out there? How do they move? What is their body shape? What are they wearing? For machines to interact with humans and the physical world, we need to train them to answer these questions. My research is focused on combining ideas from computer vision and machine learning to enable machines to perceive humans. During my Ph.D. I worked mostly on geometric modelling and articulated tracking from images.

I am responsible for the creation of websites for scientific data acquisition and dissemination related to 3D body shape, as well as web development for scientific experiments and perceptual studies. I am interested in understanding and studying how culture and other individual factors affect our perception of bodies.

My research interests are in computer vision and machine learning. I'm currently working on motion estimation.

My goal is to apply statistical human body models in various research domains such as psychology, cognitive science, and medicine. A primary goal is to make our body software accessible to more people. For this purpose I interact with various research groups who need body data and software for doing experiments. I manage these relationships, and support the transfer of body shapes as needed.

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 current research focuses on representing the appearance of people in images and video sequences. I am particularly interested in 2D and 3D models that capture the variability in shape of articulated and deformable objects like the human body. Previous work focused on color image reproduction, multispectral color imaging, readability of colored text.