Angjoo Kanazawa

I am a BAIR postdoc at UC Berkeley adviced by Jitendra Malik, Alexei E. Efros and Trevor Darrell.
I completed my PhD in CS at the University of Maryland, College Park with my advisor David Jacobs. Prior to UMD, I spent four wonderful years at NYU where I worked with Rob Fergus and completed my BA in Mathematics and Computer Science.

Since then I've had the pleasure to work with:

Peter Belhumeur at Columbia University
Google[x] self-driving car team
Manmohan Chandraker at NEC Labs in Cupertino
Michael Black at Max Planck Institute in Tübingen, Germany

Contact

Email: kanazawa at eecs.berkeley.edu
Homepage: this page | old

Research Interest

My interests are in the area of computer vision, computer graphics, and machine learning. My research focuses on single-view 3D reconstruction of non-rigid objects such as humans and animals. Animals are interesting subjects because they are highly deformable and also because their ground truth 3D data are not practical to acquire in an unconstrained environment. This motivates interesting questions like "how can we learn a model that can infer the 3D shape of non-rigid objects from a single image, just from observing 2D images and videos?" Also, animal pictures are a lot of fun to work with.

Papers

	Learning Category-Specific Mesh Reconstruction from Image Collections Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, Jitendra Malik (* equal contribution) ECCV 2018 [project page] [arXiv preprint] [video] [bibtex]
	End-to-end Recovery of Human Shape and Pose Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018 [project page with code] [pdf] [arXiv preprint] [bibtex]
	SfSNet : Learning Shape, Reflectance and Illuminance of Faces ‘in the wild’ Soumyadip Sengupta, Angjoo Kanazawa, Carlos D. Castillo, David W. Jacobs CVPR 2018 (Spotlight) [project page with code] [pdf] [arXiv preprint] [bibtex]
	Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images Silvia Zuffi, Angjoo Kanazawa, Michael J. Black CVPR 2018 (Spotlight) [project page with 3D models] [pdf]
	Towards Accurate Marker-less Human Shape and Pose Estimation over Time Yinghao Huang, Federica Bogo, Christoph Lassner, Angjoo Kanazawa, Peter V. Gehler, Javier Romero, Ijaz Akhter, Michael J. Black International Conference on 3D Vision (3DV), 2017. [pdf]
	3D Menagerie: Modeling the 3D shape and pose of animals Silvia Zuffi, Angjoo Kanazawa, David W. Jacobs, Michael J. Black Computer Vision and Pattern Recognition (CVPR) 2017. (Spotlight) [project page with model and demo] [pdf] [arXiv]
	Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero and Michael J. Black (* equal contribution) European Conference on Computer Vision (ECCV) 2016. (Spotlight) [pdf] [project page with code] [Spotlight video] Our work was featured in two minute papers!
	WarpNet: Weakly Supervised Matching for Single-View Reconstruction Angjoo Kanazawa, David W. Jacobs, Manmohan Chandraker Computer Vision and Pattern Recognition (CVPR) 2016. [pdf] [supp] [test set ids & our curves]
	Learning 3D Deformation of Animals from 2D Images Angjoo Kanazawa, Shahar Kovalsky, Ronen Basri, David W. Jacobs Eurographics 2016. Günter Enderle Best Paper Award [pdf] [code on github] [fastforward] [See the results here]
	Locally Scale-invariant Convolutional Neural Network Angjoo Kanazawa, Abhishek Sharma, David W. Jacobs Deep Learning and Representation Learning Workshop: NIPS 2014. [pdf] [code on github]
	Affordance of Object Parts from Geometric Features Austin Myers, Angjoo Kanazawa, Cornelia Fermuller, Yiannis Aloimonos RGB-D: Advanced Reasoning with Depth Cameras: RSS 2014 [pdf] [Part Affordance Dataset]
	Dog Breed Classification Using Part Localization Jiongxin Liu, Angjoo Kanazawa, Peter Belhumeur, David W. Jacobs European Conference on Computer Vision (ECCV), Oct. 2012. [pdf] [slides] try our iPhone app: Dogsnap ! Columbia dogs with parts dataset used in the paper: zip file (1.1GB) 133 breeds recognized by the American Kennel Club 8,351 images of dogs from Google image search, Image-net, and Flickr. 8 part locations annotated for each image