Previously, I was a Research Scientist at Google Research, and BAIR postdoc at UC Berkeley advised by Jitendra Malik, Alexei A. Efros and Trevor Darrell. I completed my PhD in Computer Science at the University of Maryland, College Park with my advisor David Jacobs. During my PhD, I had the pleasure to visit the Max Planck Institute in Tübingen, Germany under the guidance of Michael Black. Prior to that, I spent four years at NYU where I worked with Rob Fergus and completed my BA in Mathematics and Computer Science.
Dear prospective students: Click here for information.
Thank you for your interest in joining my group! I likely won't respond to your email if you ask me about PhD admissions, please apply directly through the department, thank you!
PhD applicants: I will be looking to hire one or two graduate students every year, so please apply through the EECS department, apply for the CS division.
Graduate or undergraduate students at UCB: Please send me an email with your interests, resume and transcripts. For undergraduates, the requirement is a solid engineering internship and CS189 or CS182/282. I prefer students who've taken some of these classes: CS184/284, CS194-26/294-26, CS280, and advanced math courses.
Interns: We are not taking visitors or students who are not at UC Berkeley at this time.
Research
My research lies at the intersection of computer vision, computer graphics, and machine learning. We live in a 3D world that is dynamic, full of life with people and animals interacting with the environment. How can we build a system that can capture, perceive, and understand these embodied agents in the 3D world from everyday photograph and video?
The goal of my lab is to answer this question.
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image Andrew Liu*, Richard Tucker*, Varun Jampani, Ameesh Makadia, Noah Snavely, Angjoo Kanazawa ICCV 2021 (Oral)
[Project Page with Demo] [Paper] [Code] [bibtex]
PlenOctrees for Real-time Rendering of Neural Radiance Fields
Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Angjoo Kanazawa ICCV 2021 (Oral)
[Project Page with Demo] [Paper] [Code] [bibtex]
Reconstructing Hand-Object Interactions in the Wild
Zhe Cao, Ilija Radosavovic, Angjoo Kanazawa, Jitendra Malik ICCV 2021
[Project Page] [Paper] [Code coming soon] [bibtex]
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++ Ruilong Li*, Shan Yang*, David A. Ross, Angjoo Kanazawa ICCV 2021
[Project Page] [Paper] [Dataset] [Code] [bibtex]
AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control Xue Bin Peng*, Ze Ma*, Pieter Abbeel, Sergey Levine, Angjoo Kanazawa SIGGRAPH 2021
[Project Page] [Paper] [Code] [bibtex]
KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa CVPR 2021 (Oral)
[Project website][Paper] [Code] [bibtex]
pixelNeRF: Neural Radiance Fields from One or Few Images Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa CVPR 2021
[Project Page/Code] [paper] [bibtex]
An Analysis of SVD for Deep Rotation Estimation Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia NeurIPS 2020
[Github] [paper] [bibtex]
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild Jason Y. Zhang*, Sam Pepose*, Hanbyul Joo, Deva Ramanan, Jitendra Malik, Angjoo Kanazawa ECCV 2020
[project page]
[Github] [arXiv preprint] [bibtex]
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild" Silvia Zuffi, Angjoo Kanazawa, Tanya Berger-Wolf, Michael J. Black ICCV 2019
[Github] [arXiv preprint] [bibtex]
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
Shunsuke Saito*, Zeng Huang*, Ryota Natsume*, Shigeo Morishima, Angjoo Kanazawa, Hao Li
(* equal contribution) ICCV 2019
[project page] [arXiv preprint] [video] [bibtex]
Predicting 3D Human Dynamics from Video
Jason Y. Zhang, Panna Felsen, Angjoo Kanazawa, Jitendra Malik ICCV 2019
[project page] [arXiv preprint] [video] [bibtex]
Learning 3D Human Dynamics from Video
Angjoo Kanazawa*, Jason Y. Zhang*, Panna Felsen*, Jitendra Malik CVPR 2019
[project page] [arXiv preprint] [video] [bibtex]
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine ICLR 2019
[project page] [code] [arXiv preprint] [video] [bibtex]
SFV: Reinforcement Learning of Physical Skills from Videos
Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, Sergey Levine ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2018)
[project page] [pdf] [BAIR Blog] [arXiv preprint] [video] [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] [bibtex]
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] [bibtex]
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] [bibtex]
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] [bibtex]
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] [bibtex]
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] [bibtex]
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] [bibtex]
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] [bibtex]
[Part Affordance Dataset] [bibtex]
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] [bibtex]
try our iPhone app:
Dogsnap ! Columbia dogs with
parts dataset used in the
paper: zip file (2.43GB)
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
Thesis
Single-View 3D Reconstruction of Animals
Angjoo Kanazawa
Doctoral Thesis, University of Maryland, August 2017
[pdf]
[slides]