About Joining My Group

I am always looking for motivated and talented students. If you are a UMich student, contact me by email and let's talk!

If you are not in a UMich degree program, please apply directly and mention my name (as well as others that interest you) in your research statement. I cannot respond to all inquiries about joining my group, but the short version is that: (1) I am currently looking for students but applications are read by a central committee. (2) I am not currently looking for visitors, summer interns, or capstone projects without a direct recommendation from someone I know.

News

• I started at the University of Michigan.
• I visited Josef Sivic and Ivan Laptev in INRIA, Willow Group, and had a wonderful time.
• Check out our new video dataset, VLOG

Research

I work on computer vision and machine learning and I am generally interested in these two areas. However, I am particularly interested in two aspects of vision. Both are motivated by the somewhat obvious observation that images are the projection of a real, physical, and 3D world to a 2D image plane of an agent. This has lead to research on understanding the world in terms of:

• physical properties: How do we recover a rich 3D world from a 2D image? I am especially interested in representations -- the answers that are obvious are also obviously defective -- as well as how we should reconcile our strong prior knowledge about this structure of the problem with data-driven techniques.
  
• functional properties: How do we infer and understand opportunities for interaction? I am interested how an agent (e.g., human or robot) can interact with the world, including in terms of what this implies for 3D understanding.

Teaching

• U. Michigan: EECS 442 (Computer Vision), Winter 2019
• UC Berkeley 294-43 (Object and Activity Recognition) Spring 2018, Fall 2017, Spring 2017. With Trevor Darrell and Alexei Efros.
• CMU 16-824 (Visual Learning and Recognition), Spring 2016. With Abhinav Gupta.
• Tutorial on 3D Scene Understanding at ECCV 2014 with Abhinav Gupta, Martial Hebert, and Derek Hoiem.
  

Publications

	D. Zhukov, J.-B. Alayrac, G. Cinbis, D.F. Fouhey, I. Laptev, J. Sivic Cross-task weakly-supervised learning from instructional videos To appear at CVPR 2019
	A. Kumar, S. Gupta, D. F. Fouhey, S. Levine, J. Malik Visual Memory for Robust Path Following NeurIPS 2018 (Oral) [Bibtex]   [Project Page]   [PDF]
	D. F. Fouhey, W. Kuo, A. A. Efros, J. Malik From Lifestyle VLOGs to Everyday Interactions CVPR 2018 [Bibtex]   [Project Page]   [Arxiv]
	S. Tulsiani, S. Gupta, D. F. Fouhey, A. A. Efros, J. Malik Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene CVPR 2018 [Project Page]   [Arxiv]   [Bibtex]
	M. Lescroart, D. F. Fouhey, J. Malik Convolutional neural networks represent shape dimensions -- but not as accurately as humans Abstract at VSS 2018
	S. Gupta, D.F. Fouhey, S. Levine, J. Malik Unifying Map and Landmark Based Representations for Visual Navigation Arxiv 2017 [Project Page]   [Arxiv]   [Bibtex]
	D. F. Fouhey, A. Gupta, A. Zisserman From Images to 3D Shape Attributes Accepted in TPAMI, Pre-print on Arxiv The TPAMI version has ugly typesetting (full-width tables on the bottom?) that I was unable to change. Read the Arxiv one. [Arxiv]   [Bibtex]
	R. Girdhar, D. F. Fouhey, M. Rodriguez, A. Gupta Learning a Predictable and Generative Vector Representation for Objects In ECCV 2016 (Spotlight) [Publication (PDF)]   [Bibtex] [Project Page]
	D. F. Fouhey Factoring Scenes into 3D Structure and Style Doctoral Dissertation [Dissertation (PDF)]   [Bibtex] [Defense Slides (PDF)]
	D. F. Fouhey, A. Gupta, A. Zisserman 3D Shape Attributes In CVPR 2016 (Oral - Watch the presentation on Youtube) [Publication (PDF)]   [Bibtex] [Project Page]   [Poster (PDF)]   [Talk (PPTX)]   [Talk (PDF)]
	R. Girdhar, D. F. Fouhey, K. M. Kitani, A. Gupta, M. Hebert Cutting through the Clutter: Task-Relevant Features for Image Matching In WACV 2016. [Publication (PDF)]   [Bibtex]
	D. F. Fouhey, W. Hussain, A. Gupta, M. Hebert Single Image 3D Without a Single 3D Image In ICCV 2015. [Publication (PDF)]   [Bibtex] [Poster (PDF)]   [Supplemental (PDF)]   [Bonus Details (PDF)]
	X. Wang, D. F. Fouhey, A. Gupta Designing Deep Networks for Surface Normal Estimation In CVPR 2015. [Publication (PDF)]   [Bibtex]
	D. F. Fouhey, A. Gupta, and M. Hebert Unfolding an Indoor Origami World In ECCV 2014 (Oral - Watch the presentation on VideoLectures.net) [Publication (PDF)]   [Bibtex] [Project Page]   [Extended Results (PDF)]
	D. F. Fouhey and C.L. Zitnick Predicting Object Dynamics in Scenes In CVPR 2014. [Publication (PDF)]   [Bibtex] [Poster (PDF)]   [Supplemental (PDF)]
	D. F. Fouhey, V. Delaitre, A. Gupta, A. Efros, I. Laptev, and J. Sivic. People Watching: Human Actions as a Cue for Single View Geometry. Accepted in IJCV (extended version of ECCV 2012 paper). [Preprint (PDF)]   [Final version (via Springer)]
	D. F. Fouhey, A. Gupta, and M. Hebert Data-Driven 3D Primitives for Single Image Understanding In ICCV 2013. [Publication (PDF)]   [Bibtex] [Project Page]   [Poster (PDF)]
	D. F. Fouhey, V. Delaitre, A. Gupta, A. Efros, I. Laptev, and J. Sivic. People Watching: Human Actions as a Cue for Single View Geometry. In ECCV 2012 (Oral - Watch the presentation on VideoLectures.net ). [Publication (PDF)]   [Bibtex] [Project Page]
	V. Delaitre, D. F. Fouhey, I. Laptev, J. Sivic, A. Gupta, and A. Efros. Scene Semantics from Long-term Observation of People. In ECCV 2012. [Publication (PDF)]   [Bibtex] [Project Page]
	D. F. Fouhey, A. Collet, M. Hebert, and S. Srinivasa Object Recognition Robust to Imperfect Depth Data. In CDC4CV 2012 in conjunction with ECCV 2012. [Publication (PDF)]   [Bibtex] [Supplemental(PDF)]   [Supp. Video 1]   [Supp. Video 2]
	M. Costanza-Robinson, B. Estabrook, and D. F. Fouhey Representative elementary volume estimation for porosity, moisture saturation, and air-water interfacial areas in unsaturated porous media: Data quality implications (Sorry for not posting a pre-print!) In Water Resources Research, Volume 47, 2011. [Official Version]   [Bibtex]
	D. F. Fouhey, D. Scharstein, and A. Briggs. Multiple plane detection in image pairs using J-linkage. In ICPR 2010. [Publication (PDF)]   [Bibtex] Implementation (Python and C) [Code (Zip)]   [Poster (PDF)]

Miscellaneous

You may be interested in the following.

Writing (arranged in chronological order):

• My Ph.D. dissertation, Factoring Scenes into 3D Structure and Style (Dissertation and Defense Slides)
  
• A note on some practical considerations when evaluating surface normals
• My A.B. thesis, Multi-Model Estimation in the Presence of Outliers (Thesis and Poster and Presentation)

Miscellaneous

• CMU RI Thesis Template (zip) based off of a CMU RI tech report template from Daniel Morris.

Joke Papers

Sometimes when I feel a creative itch, the end result is a joke publication (which despite the name often have a serious point to make). These are all done with the one and only Daniel Maturana.

• Keras4Kindergartners.com: Check out our secret backup plan for if research doesn't work out.
• Deep Excel: Everybody knows that deep learning brings about synergy and so does Excel, so Daniel Maturana and I released ExcelNet, a break-through technology that merges the power of Deep Learning with Excel.
  The spreadsheet   The whitepaper   The pitch slides   Protips (actually quite helpful)  
• Visual Rank Estimation: Visually Identifying Rank with Daniel Maturana proves that linear algebra can be replaced with machine learning. It also shows that if you are a CNN, the much-hated jet colormap is actually the best colormap. Winner of the ``Most Frighteningly Like Real Research'' award at SIGBOVIK 2016.
• Cat Basis Purrsuit: I've been told that this is the highlight of my research career and it can only go downhill from here: Cat Basis Pursuit
• Celebrity Learning: You may also know my award-winning work with Daniel Maturana on celebrity-themed learning, making money at home from Hilbert's Nulstellensatz, and more from OneWeirdKernelTrick.com
• Kardashian Kernel: The original, rarely imitated, never duplicated. Originator of the alphabetically-related-work section: The Kardashian Kernel

Fun & Games

• Free parking in Berkeley: Parking people hate him. Find out how to park on Berkeley's campus for free with this 1 weird trick.
• Caffe64: dependency-free deep learning in < 12KB via the magic of assembly. Clone and star it here
• miniml: Everybody these days has a deep learning toolkit that takes forever to download, has tons of dependencies, and produces inscrutable models. In the spirit of simplicity and transparency, I wrote a new package that's only 2172 bytes when compiled, has zero dependencies (not even the C standard library -- that's total bloat), and fits a hidden-layer-free network aka logistic regression. Meet miniml here: miniml .
• Programming in Postscript?: Found in the archives, a postscript file (can.ps)that solves The Missionaries/Cannibals problem, then renders the solution. Send it to a printer, open it in vim, be amazed at how much time I had as an undergrad.
• Revisiting Monet in Light of that Other Painter of light: Monet is so booooring and tranquil. Click here to see him spiced up to look like Thomas Kinkade
• Academic Ancestry and Erdös Number: See where I fit in!
• Sculpture at Berkeley: 3D shapes are fun and Berkeley has a lot of them
• #1 Messiest Desk: My desk was voted #1 messiest desk in the second floor of Smith Hall at Carnegie Mellon during the RI's open house for 2016.
  If a cluttered desk is an indication of a cluttered mind, then what does an empty desk indicate?
• Award-Winning Optimization Sheet: I wrote an award-winning one page cheat-sheet for Convex Optimization at CMU (10-725). Be sure to check the watermark!