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.
I am interested in the intersection between computer vision and machine learning with a focus on holistic visual scene understanding. In particular, I am interested in analyzing and modeling people in our complex visual scenes.
I am leading the project AirCAp: Micro Aerial Vehicles (MAV)-based Outdoor motion CAPture. In this project, we have developed MPC-based formation-control methods to jointly perceive moving targets through multiple MAVs, each equipped with a monocular camera. Real robot experimental results have not only validated our approach but have set a strong foundation for future research direction in this context. For further information, please visit the project page.
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.
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.
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.
I'm a student assistant supervised by postdoc researcher Timo Bolkart. My work is focusing on gaze behavior extraction and animation. Currently, one of our goals is to integrate human gaze behavior to the face model.
I am interested in human object interaction learning, to start with human ground interaction leaning (walking/ running) and later on would like to extended it to more complex hand manipulated objects.
I am a PhD student of Dr. Michael J Black and I am interested in estimating human bodies and the objects they interact with from images.
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.
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.
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.
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.
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.
Phd student from Georgia Tech, working on 3D scene flow and motion estimation.
From June 2018 I am a PhD student supervised by Prof. Michael Black. Before that, I finished my Master's study in Optics and Photonics at Karlsruhe Institute of Technology, and Bachelor's study in Physics at Peking University.
I am working as a student assistant within the AirCap (Aerial outdoor motion capture) project. The project goal is developing a 3D shape and motion capture system in outdoor scenarios using multiple co-operative UAVs. Possible applications of this system are autonomous search and rescue robot teams and autonomous systems for crowd supervision. My duties include integrating sensors into the current distributed system and taking care of software and hardware repositories. Currently, I am taking part in the design and implementation of a controller module for the Blimp, which should be integrated in the current UAV system.
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 work on decomposing photographs into their intrinsic layers of reflectance and shading using deep learning methods for fast inference. In addition I started to work on interactive semantic segmentation using CNNs.
My work spans both the research aspect of creating the world most realistic human body models and the development of computationally efficient and scalable software that enables learning such models from large scale data sets. I completed an MSc in Statistics at Imperial College London, MSc in Artificial Intelligence at the Uni. of Manchester and BEng in Mechatronics and Robotics at the University of Liverpool.
I'm a second year PhD Student at the department of Perceiving Systems. I'm developing multi-aerial vehicle intelligence for practical research application with prototypes built here at the institute. My current work involves integrating detections from real time deep neural networks into cooperative multi vehicle sensor fusion.
My current research is focused on building probabilistic models on top of deep neural networks for various computer vision tasks. I'm also interested in object detection and recognition, as well as general machine learning algorithms and applications.
My research aims at understanding the world through the capture and analysis of heterogeneous data (MRI, CT, Point clouds, images, ...) in order to create applied digital instruments, that allow, for example, to generate novel views from a scene, to infer the human shape from a clothed scan, or to predict the amount of adipose tissue of a person from surface observations. To address this challenge, I adopt the approaches of Computer Vision, Signal Processing, Computer Graphics and Statistical Models. My research is often multi-disciplinary, as I need to combine knowledge from these different domains.
I am interested in understanding how we perceive human body shape and pose. Furthermore, I am interested in studying how individual factors (such as culture) affect our perception of our own and other people's bodies. On the side from research, I enjoy web technologies! I am currently supporting 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.
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. Since completing my PhD I'm focusing in human hand modeling, detection and pose estimation.
My research interests are in motion estimation and scene understanding. In particular I'm interested in exploring and modeling how the semantics and the motion of the scene are related.
My research is based in preclinical imaging at the Werner Siemens Imaging Center, and I am focused on novel molecular imaging techniques. My research involves awake and unrestrained rodents and measurements of a more truthful neurophysiological response (to drugs, stimuli, treatments, etc…). I am interested in building an model for tracking and capturing the most commonly used research rodents in preclinical applications.
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 modeling and capturing motions with a focus on haptic motion capturing. More specifically, I am interested in implementing hands and body interaction( touch and pressure) into motion capturing systems.
I am working with Dr. Aamir Ahmad on the problem of Multi-Robot Obstacle Avoidance for Target Tracking Scenarios using Model-Predictive Optimization
The goal of my research is to understand visual factors that contribute to one’s representation of the physical body. I am using the novel technology of biometric-based avatars provided by the MPI for Intelligent Systems to study mechanisms related to body representations in healthy people and its distortions in eating and weight disordered individuals.
For my PhD I worked with Juergen Gall on Hand-Object Interaction. In particular we focused on capturing the motion of hands interacting with each other and/or with a rigid or an articulated object. We further studied the case of acquiring missing knowledge about the manipulated object, i.e. its shape or its kinematic model.
I am interested in human understanding in videos. Particularly, I am exploring the use of synthetic images for learning human-related representations.
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.