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Computer Vision

Teaching computers to understand the visual world


We want to change the way you interact with visual data.   We want to make your photos magical, we want to deeply understand images and videos from cameras everywhere: in your phone, on your Xbox, in your fridge, on robots, in cars, anywhere.   We want you to be able to find your stuff, answer questions, make fantastic new images.  And we do that by inventing new algorithms and thinking of new mathematical models for how images come to be.


Image understanding

Understanding images

Image understanding with tens of layers, millions of classes, billions of images.


Human motion capture for Kinect

Understanding Humans

So much of computer vision is ultimately for humans, images of humans are an important special case

Image and video editing

Making images better

Pictures are an important part of our lives, and computer vision gives us the tools to enjoy better pictures.

Discrete optimization

Learning and Optimization

Computer vision often requires the solution of especially large or difficult problems in machine learning and nonlinear optimization, and we innovate in these domains.


Models for Video

One view of video is "all of the above, but faster".   We also try to explore new representations of video and new modes of interaction


Where are we?

Localization problems occur everywhere, from augmented reality to medical imaging to 3D modelling.


Recent vision publications

Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, Pushmeet Kohli, Vladimir Tankovich, and Shahram Izadi, Fusion4D: Real-time Performance Capture of Challenging Scenes, SIGGRAPH, July 2016.

Jonathan Taylor, Lucas Bordeaux, Thomas Cashman, Bob Corish, Cem Keskin, Eduardo Soto, David Sweeney, Julien Valentin, Benjamin Luff, Arran Topalian, Erroll Wood, Sameh Khamis, Pushmeet Kohli, Toby Sharp, Shahram Izadi, Richard Banks, Andrew Fitzgibbon, and Jamie Shotton, Efficient and Precise Interactive Hand Tracking through Joint, Continuous Optimization of Pose and Correspondences, in ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques, July 2016.

Y. Lewenberg, Y. Bachrach, S. Shankar, and A. Criminisi, Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information, in Intl Joint Conference on Artifical Intelligence (IJCAI), July 2016.

Tatsunori Taniai, Sudipta N Sinha, and Yoichi Sato, Joint Recovery of Dense Correspondence and Cosegmentation in Two Images, IEEE – Institute of Electrical and Electronics Engineers, 26 June 2016.

Jaesik Park, Yu-Wing Tai, Sudipta N Sinha, and In So Kweon, Efficient and Robust Color Consistency for Community Photo Collections, in Computer Vision and Pattern Recognition (CVPR), IEEE – Institute of Electrical and Electronics Engineers, 26 June 2016.

More publications...