My research focuses on computing and understanding motion in the world from video. In generic scenes I study optical flow (the 2D image motion) and how it relates to physical properties of the world including 3D shape, material, illumination, and motion. I also develop new methods to capture natural, complex, human and animal motion for applications in computer vision, animation, and neuroscience.

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.

Leader of the Autonomous Learning Group

I am personal assistant to Prof. Stefan Schaal and provide administrative support fo the Autonomous Motion Department.

Back from parental leave, I am happy to continue working as a personal assistant to Professor Bernhard Schölkopf and to the lab members of his Empirial Inference Department, together with my colleague Diana Rebmann.

My research focuses on perception for autonomous robotic manipulation and grasping. I believe that these methods have to be goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning.

Sebastian Trimpe is a Senior Research Scientist and Group Leader at the Max Planck Institute for Intelligent Systems. He leads the Intelligent Control Systems group within the Autonomous Motion Department.

I am currently leading the Probabilistic Learning Group in the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen.

My main interests include computer vision and machine learning. I focus on depth estimation and 3D reconstruction, with a mix between applied and more theoretical research.

Advisor: Andreas Geiger

Hi! My name is Timothy, I am student of physics at the Karlsruhe Institute of Technology (KIT). I joined Bernhard Schölkopf's group at the MPI-IS in July 2017 for my master's thesis of 12 months, which will be about with improving the search for gravitational waves from compact binary coalescences at the LIGO experiment using machine learning.

Advisor: Jeannette Bohg

My main research interests lie in Bayesian sensor fusion and optimization-based state estimation. The core application focus of my research is vision-based perception in Robotics. I have worked with several ground robots in the recent past. Currently, I am focusing on aerial robots and active perception in multiple aerial vehicle systems.

I work with computer vision researchers to coordinate, schedule and run human subjects trials involving body shape and motion analysis at the Perceiving Systems Department. To collect data we use several computer vision technologies, including our unique 3D and 4D body scanners and our new 4D face scanner.

Advisor: Philipp Hennig

Advisor: Philipp Hennig

Advisor: Sebastian Trimpe

Advisor: Andreas Geiger

My research is in the area of Computer Vision and Machine Learning. Currently, I am working on problems related to estimation of 3D motion fields from stereo image sequences.

Scientific programmer, roboticist

I am a technician working one half each in the groups of Bernhard Schölkopf and Heinrich Bülthoff. In the Bülthoff Department I support the experiments of Isabelle Bülthoff who is working on face recognition.

My research focus and interest is in the area of 3D computer vision and computer graphics. I am especially interested in non-rigid shape analysis, statistical modelling of various kinds of shapes, and the analysis of motion data.

Advisor: Raffi Enficiaud

Advisor: Jan Peters

I have a broad interest in machine learning and applied mathematics. I am particularly interested in how the field of causal inference can benefit traditional machine learning problems such as transfer learning and reinforcement learning.

Advisor: Andreas Geiger

I'm working on "Efficient Invariant Deep Models for Computer Vision" in cooperation with Bosch.

Advisor: Michael Black

My research focuses on understanding the link between semantics and vision. I believe that our intelligence and ability to perceive our surroundings is strongly influenced by language and meaning. I am also very interested in human emotions, facial expressions, sentiment analysis, multimodal learning, transfer learning and 3D modelling of human bodies and faces; amongst others.

Advisor: Sebastian Trimpe

Central Scientific Facility Software Workshop

I'm a student assistant supervised by postdoc researcher Timo Bolkart, we are working on audio-driven facial shape and expression

Advisor: Peter Vincent Gehler

Advisor: Raffi Enficiaud

I am doing a PhD in the field of machine learning and robotics. I work in the empirical inference department and my supervisor is Jan Peters. Our aim is to build reinforcement learning algorithms that can learn control policies for complex robotics tasks such as robot table tennis. For that we need to include as much prior knowledge as possible into our models, since experiments on the real robot are usually expensive. My interest are mostly in machine learning, reinforcement learning and Bayesian inference. But I also like algorithms and math, and before coming to the MPI I used to participate in competitions on these topics.

PhD student in Cambridge-Tuebingen program with primary research focus in deep learning, reinforcement learning, approximate inference, and robotics.

Advisor: Andreas Geiger

I am interested in estimating 3D scene representations from multi-view video sequences. In particular, I focus on combining semantic segmentation, object detection and classification with 3D reconstruction using efficient inference methods.

I am a PhD student of Dr. Michael J Black and I am interested in 3D reconstruction in the context of user interaction.

Brain-Computer Interface for ALS Patients

Advisor: Michael Black

Yinghao Huang is a PhD candidate at Max Planck Institute for Intelligent Systems, supervised by Director Michael J. Black. His research interests fall in the areas of Machine Learning, Computer Vision and Computer Graphics. More specifically, he focuses on the topics of Human Body Modelling, 3D Human Shape and Pose Estimation, and other related things.

Advisor: Andreas Geiger

Perception is a fundamental part of intelligence since perception is necessary to acquire knowledge and knowledge is necessary to understand perception. Therefore computer vision is one of the most important aspects in the realization of intelligent systems. My interest of research lies in computer vision and the combination with machine learning which, to my mind, will enable the realization of intelligent systems. Currently, I am working on optical flow and how to incorporate high-level information to alleviate this ill-posed problem.

Advisor: Jeannette Bohg

I am a PhD student at Empirical Inference Department, Max Planck Institute for Intelligent Systems and Werner Siemens Imaging Center, UKT, Tuebingen. My work focuses on the assessment of tumor heterogeneity using multi-modality (PET/MR) imaging for personalized cancer therapies. I am also very interested in applying statistical methods on the data originating from multi-modal and heterogeneous sources. Before moving to Germany, i obtained my masters degree in systems engineering from Politecnico Di Milano, Italy.

I coordinate our department's research trials. We collect data on human body shape and pose. This includes 3D and 4D body scans, face scans, anthropometric measurements, Motion Capture, and further (experimental) technologies. I recruit participants, schedule, manage appointments, create protocols of poses and movements and gain data according to our scientists' needs. Also, I process the data and take care of data security. Besides that, I am responsible for organising lab tours for visitors of our department.

Advisor: Philipp Hennig

PhD student

My research concerns learning models of perception and production of non-verbal communicative behavior. Such models can be used to create richer human-robot and human-avatar interaction, for medical diagnosis systems, and for contextual synthesis of different kinds of human behaviors, e.g., guiding synthesis of hand motion from body motion.

Advisor: Jeannette Bohg

Advisor: Jan Peters

Advisor: Sebastian Trimpe

Advisor: Peter Vincent Gehler

How can autonomous perception discover high-dimensional patterns in recorded data from our environment? I am approaching this question by working on structured computer vision tasks, such as Human Pose Estimation. I hope that insights from this area will improve our data analysis systems, so that they can assist us in better understanding our environment.