Advisor(s):
Michael Black
My research focuses on Deep Learning for dense estimation problems such as Optical Flow. My broad interests include Computer Vision and Machine Learning. My homepage is located here.
Faces, their shape, and their motion are essential to communication. Consequently, we want a model of the face that can capture the full range of face shapes and expressions. Such a model should be realistic, easy to animate, easy to fit to data, and should support inference about human emotion and speech....
Michael Black Timo Bolkart Anurag Ranjan Soubhik Sanyal Tianye Li Javier Romero Cassidy Laidlaw
Much of our work focuses on 3D models of objects and scenes. We would like to take advantage of current deep learning approaches in representing and reasoning about 3D. Unfortunately, the standard 2D convolutional models do not readily extend to 3D because they do not match well current 3D data structures....
Michael Black Gernot Riegler Osman Ulusoy Andreas Geiger Anurag Ranjan Timo Bolkart Soubhik Sanyal
Deep learning has brought rapid progress for many computer vision problems but current methods require large training datasets with annotated ground truth. Human annotators tend to be reasonably efficient for tasks like sparse 2D joint estimation, however annotation for other tasks like dense optical...
Javier Romero Anurag Ranjan Michael Black Jonas Wulff David Hoffmann Dimitris Tzionas Siyu Tang Naureen Mahmood
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Javier Romero Anurag Ranjan Michael Black Jonas Wulff David Hoffmann Dimitris Tzionas Siyu Tang Naureen Mahmood Gul Varol Cordelia Schmid
We view optical flow as the projection of the 3D motion field into the image plane. Until recently, optical flow algorithms were designed by hand and incorporated various heuristics. Deep learning methods provide an opportunity to move away from hand-crafted models but have several limitations. The key one...
Michael Black Andreas Geiger Anurag Ranjan Jonas Wulff Deqing Sun Varun Jampani Laura Sevilla Joel Janai Fatma Güney
Historically, optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the 2D image motion. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. For rigid objects, the motion is related to the ...
Michael Black Jonas Wulff Anurag Ranjan Laura Sevilla Fatma Güney Varun Jampani Andreas Geiger Deqing Sun
Learning to solve optical flow in an end-to-end fashion from examples is attractive as deep neural networks allow for learning more complex hierarchical flow representations directly from annotated data. However, training such models requires large datasets and obtaining ground truth for real images is challenging as labeling dense ...
Joel Janai Fatma Güney Anurag Ranjan Michael Black Andreas Geiger
Ranjan, A., Bolkart, T., Sanyal, S., Black, M. J.
In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11207, pages: 725-741, Springer, Cham, September 2018 (inproceedings)
Ranjan, A., Romero, J., Black, M. J.
In 29th British Machine Vision Conference, September 2018 (inproceedings)
Ranjan, A., Jampani, V., Kim, K., Sun, D., Wulff, J., Black, M. J.
May 2018 (article)