The Breakdown: LiveSplats lets audiences experience sports events in 3D. Fans can choose their own camera angles for an immersive experience instead of watching on a flatscreen. The system uses advanced rendering techniques to recreate scenes with high visual fidelity. * * * LiveSplats allows fans to follow sports events from any angle using [...]
Our group is interested in inferring physically and spatially grounded representations from perceptual input, and leveraging these for advances in fundamental problems in computer vision and robot manipulation. We believe that to enable machines to understand the physical world, we should reduce the reliance on supervision by annotation, and instead develop learning mechanisms informed by [...]
Our group studies Artificial Intelligence at the intersection of Computer Vision, Machine Learning & Robotics. Our ultimate goal is to build agents with a human-like ability to generalize in real and diverse environments. We believe understanding how to continually develop knowledge and acquire new skills from just raw sensory data will play a vital role [...]
The Generative Intelligence Lab studies the collaboration between Human Creators and Generative Models, with the goal of building intelligent machines capable of helping everyone tell their visual stories. We are studying the following questions: Interaction between creators and generative models: How can we help creators control the model outputs more easily? We develop algorithms and interfaces [...]
For more information, please visit Laszlo Jeni's CUBE Lab Site.
Our group is interested in inferring physically and spatially grounded representations from perceptual input, and leveraging these for advances in fundamental problems in computer vision and robot manipulation. We believe that to enable machines to understand the physical world, we should reduce the reliance on supervision by annotation, and instead develop learning mechanisms informed by [...]
Group for research in computational imaging, illumination, display, light transport, microscopy, and more. For a full list of people from departments outside of the Robotics Institute, please visit the team page from the group website.
At Generative Intelligence Lab, we are studying the collaboration between Human Creators and Generative Models, with the goal of building intelligent machines capable of helping everyone tell their visual stories. We are studying the following questions: Interaction between creators and generative models: How can we help creators control the model outputs more easily? We [...]
My ultimate goal is to build agents with a human-like ability to generalize in real and diverse environments. I believe understanding how to continually develop knowledge and acquire new skills from just raw sensory data will play a vital role in achieving this goal. I draw inspiration from psychology to build practical systems at the [...]
Research Topics Computer Vision Light-weight Deep Learning Visual Perception on Moving Platforms Wireless and Wearable Sensing Applications Computer Vision Surveillance & Security Field Robotics Health Care Advances in sensors, machine learning and computer vision hold tremendous potential to reshape our world. DeLight focuses on unlocking this potential contained in applied machine learning approaches, and applying [...]
I am an Assistant Professor with the Robotics Institute and Computer Science Department in the School of Computer Science at Carnegie Mellon University. My research interests span many topics across computer graphics and computer vision, but I'm particularly interested in computational imaging: a field that combines optics, electronics, and computational processing to capture or display [...]
My research lies at the intersection of robotics, machine learning, and computer vision. I am interested in developing methods for robotic perception and control that can allow robots to operate in the messy, cluttered environments of our daily lives. My approach is to design new deep learning / machine learning algorithms to understand environmental changes: [...]
The community of researchers doing Computer Vision @ CMU.
The Illumination and Imaging (ILIM) Laboratory at the Robotics Institute is dedicated to the study of light transport and the development of novel illumination and imaging technologies. The laboratory is part of the broader computer vision and computer graphics groups at Carnegie Mellon. Our research is motivated by applications in the areas of digital imaging, [...]
The Biomedical Image Guidance (BIG) Lab conducts research in biomedical optics, image analysis, and visualization, especially for intraoperative guidance and also for diagnosis. We use computer vision to assist human placement of biomedical scanners, and we use novel visualization of biomedical images to better guide human manipulation of tools. Applications are wide-ranging, from computer-vision for [...]
My research interests are in Computer Vision, Digital Humans, and Computational Behavior Science, specifically in areas of modeling, understanding, and synthesizing human motion and behavior using diverse sensors. Currently I am directing the CUBE Lab. The research in the Computational Behavior (CUBE) Lab broadly focuses on computer vision and machine learning for behavior science and [...]
My research focuses on computer vision, often motivated by the task of understanding people from visual data. My work tends to make heavy use of machine learning techniques, often using the human visual system as inspiration. For example, temporal processing is a key component of human perception, but is still relatively unexploited in current visual [...]
My research focuses on the physics of computer vision and computer graphics. My projects highlight three main aspects of my research – the mathematical modeling of the interactions of light with materials and the atmosphere; the design of novel cameras and programmable lighting; and the development of algorithms for rendering and interpreting scene appearance. My [...]
Research interests: computer vision, human activity forecasting, first-person vision, inverse reinforcement learning, deep learning, assistive technologies for people with visual impairment
Project Highlights Face-Alignment, a 2010 demo using a face map of President Obama was posted to the CMU Robotics YouTube Channel New York Times Article about Kanade's Virtualized RealityTM at SuperBowl XXXV EyeVision Video from Super Bowl Broadcast on YouTube My research interests are in the areas of computer vision, visual and multimedia technology, and robotics. [...]
Perception is a fundamental challenge for mobile robots navigating through and interacting with their environment. My research focuses on 3D mapping and localization using information from any available sensor, including vision, laser, inertial, GPS and sonar (underwater). To enable online operation, my research also explores novel algorithms for efficient and robust inference at the intersection [...]
Dr. De la Torre's research interests include machine learning, signal processing and computer vision, with a focus on understanding human behavior from multimodal sensors (e.g. video, body sensors). I am particularly interested in three main topics: Component Analysis (CA): CA methods (e.g. kernel PCA, Normalized Cuts, Multidimensional Scaling) are a set of algebraic techniques that decompose [...]
Efficient techniques for object/category recognition Use of contextual information, in particular 3-D geometry from images, for scene analysis Symbolic knowledge for scene interpretation and reconstruction Motion analysis for feature extraction and event detection in video clips Efficient tools for the analysis of dynamic 3-D point clouds ("3-D signal processing") Perception for autonomous systems Detection, tracking, [...]
How do we represent the visual world? My research focuses on developing representation and reasoning approaches for deeper understanding of the scene. I am interested in formulating the scene understanding problem in terms of the underlying 3D scene and develop reasoning approaches based on physical, functional and causal relationships between the different elements in the [...]
I work broadly in computer vision and computer graphics, but I focus on computational imaging: this is the joint design of optics, electronics, and computation to create imaging systems with unprecedented capabilities. Some examples include: imaging systems that can see around corners or through skin; passive 3D sensing systems with extreme resolution; ultrafast programmable lenses; [...]
CV, Research Statement, and Teaching Statement I am a Systems Scientist and Adjunct Assistant Professor at Carnegie Mellon University (CMU), directing the Biomedical Image Guidance Laboratory and teaching an internationally recognized graduate course on medical image analysis algorithms. I have a Ph.D. in Robotics and a B.S. and M.S. in computer engineering. My primary appointment [...]
