Pinned Repositories
chumpy
dotfiles
subject to constant additions.
neural_renderer
Code for the paper "Neural 3D Mesh Renderer" (CVPR 2018) by H. Kato, Y. Ushiku, and T. Harada.
PPO
Pytorch implementation of proximal policy optimization
rlkit-diayn
Collection of reinforcement learning algorithms
tm157's Repositories
tm157/dotfiles
subject to constant additions.
tm157/PPO
Pytorch implementation of proximal policy optimization
tm157/chumpy
tm157/neural_renderer
Code for the paper "Neural 3D Mesh Renderer" (CVPR 2018) by H. Kato, Y. Ushiku, and T. Harada.
tm157/rlkit-diayn
Collection of reinforcement learning algorithms
tm157/3d-pose-baseline
A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.
tm157/cap2vid
Attentive Semantic Video Generation using Captions
tm157/DensePose
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body
tm157/gym
A toolkit for developing and comparing reinforcement learning algorithms.
tm157/mil
Code for "One-Shot Visual Imitation Learning via Meta-Learning"
tm157/neural_renderer-1
A PyTorch port of the Neural 3D Mesh Renderer
tm157/oh-my-zsh
A delightful community-driven (with 1,000+ contributors) framework for managing your zsh configuration. Includes 200+ optional plugins (rails, git, OSX, hub, capistrano, brew, ant, php, python, etc), over 140 themes to spice up your morning, and an auto-update tool so that makes it easy to keep up with the latest updates from the community.
tm157/pix2pixHD
Synthesizing and manipulating 2048x1024 images with conditional GANs
tm157/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
tm157/rllab
rllab is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym.
tm157/roboschool
Open-source software for robot simulation, integrated with OpenAI Gym.
tm157/surreal
Learning from Synthetic Humans, CVPR 2017
tm157/tm157.github.io
tm157/ViZDoom
Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information. :godmode: