Discover @akanazawa Open Source projects

@akanazawa

akanazawa

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Registered over 13 years ago
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Python
38.1 %

MATLAB
28.6 %

C++
19.0 %

Emacs Lisp
9.5 %

Ruby
4.8 %

hmr

Project page for End-to-end Recovery of Human Shape and Pose

human_dynamics

Project for paper "Learning 3D Human Dynamics from Video"

cmr

Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections

motion_reconstruction

Motion Reconstruction Code and Data for Skills from Videos (SFV)

vgan

Code for image generation of Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

catdeform

A Matlab implementation for the paper: "Learning 3D Deformation of Animals from 2D Images" http://www.umiacs.umd.edu/~kanazawa/papers/cat_eg2016.pdf

Semantic-texton-forests

Implementation of semantic texton forests in matlab

MRF

simple implementation of MRF foreground/background segmentation for CMSC828 Spring '12

Structure-from-Motion

Affine structure from motion in image sequences adapted from Derek Hoiem's CS 543/ECE 549 course at the University of Illinois at Urbana-Champaign

si-convnet

Implementation of the [Locally Scale-Invariant Convolutional Neural Network](http://www.umiacs.umd.edu/~kanazawa/papers/sicnn_workshop2014.pdf)

Intrinsic-Image

experiments with intrinsic image decomposition

Topic-Random-Field

Implementation of "Image Segementation with Topic Random Field" by Zhao, Fei-fei, and Xing.

Explicit_Shape_Regression

Implementation of the paper for face alignment by MSRA research.microsoft.com/en-us/.../cvpr12_facealignment.pdf

Basic-Classification

ML Fall '10 project 1

Class-Notes

All of my class notes!

Nonlinear-Diffusion

bare minimum illustration of 3 nonlinear image denoising methods

Complex-Classification

ML Fall '11 project 2

Collective-Classification

Instagram-Scraper

Scrape images from instagram

Formal-Reasoning-in-Computer-Science

University of Maryland open study group on mathematical proofs, theoretical arguments, probability theory and randomized algorithms. Focus on solving problems. Content has been chosen to complement graduate-level work in computer science.

Unsupervised-Learning

UMD Fall '11 CMS726 Machine Learning Project 3 with Hall Daume.