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🇫🇷 Oh My Tmux! Pretty & versatile tmux configuration made with ❤️

archjbald.github.io

cihp_pgn

Code repository for Part Grouping Network, ECCV 2018

cocoapi

Clone of COCO API - Dataset @ http://cocodataset.org/ - with changes to support Windows build and python3

first-contributions

🚀✨ Help beginners to contribute to open source projects

human-pose-estimation.pytorch

The project is an official implement of our ECCV2018 paper "Simple Baselines for Human Pose Estimation and Tracking(https://arxiv.org/abs/1804.06208)"

imaginator

[WACV 2020] ImaGINator: Conditional Spatio-Temporal GAN for Video Generation

mspn

Multi-Stage Pose Network

pise

pose-transfer

Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19 (Oral).

posestylizer

PyTorch implementation of Generating Person Images with Appearance-aware Pose Stylizer (IJCAI 2020)

rsn

unipose

We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.