Displaying 1 to 11 from 11 results

tf-pose-estimation - Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference

  •    PureBasic

'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.

openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation

  •    C++

OpenPose represents the first real-time multi-person system to jointly detect human body, hand, and facial keypoints (in total 135 keypoints) on single images. For further details, check all released features and release notes.

Realtime_Multi-Person_Pose_Estimation - Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)

  •    Jupyter

By Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. Code repo for winning 2016 MSCOCO Keypoints Challenge, 2016 ECCV Best Demo Award, and 2017 CVPR Oral paper.

trt_pose - Real-time pose estimation accelerated with NVIDIA TensorRT

  •    Python

Pre-trained models for human pose estimation capable of running in real time on Jetson Nano. This makes it easy to detect features like left_eye, left_elbow, right_ankle, etc. Training scripts to train on any keypoint task data in MSCOCO format. This means you can experiment with training trt_pose for keypoint detection tasks other than human pose.




human-pose-estimation

  •    Python

This is an official pytorch implementation of Simple Baselines for Pose Estimation and Pose Tracking. This work provides baseline methods that are surprisingly simple and effective, thus helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. On COCO keypoints valid dataset, our best single model achieves 74.3 of mAP. You can reproduce our results using this repo. All models are provided for research purpose. The code is developed using python 3.6 on Ubuntu 16.04. NVIDIA GPUs ared needed. The code is developed and tested using 4 NVIDIA P100 GPUS cards. Other platform or GPU card are not fully tested.

binary-human-pose-estimation - This code implements a demo of the Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources paper by Adrian Bulat and Georgios Tzimiropoulos

  •    Lua

This code implements a demo of the Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources paper by Adrian Bulat and Georgios Tzimiropoulos. Download the model available bellow and place it in the models folder.

human-pose-estimation - This repository implements a demo of the Human pose estimation via Convolutional Part Heatmap Regression paper

  •    Lua

This repository implements a demo of the Human pose estimation via Convolutional Part Heatmap Regression paper Bulat&Tzimiropoulos. For sh and fb.python packages please visit their github repositories and carrefully follow the instruction provided by their authors.


deepstream_pose_estimation - This is a sample DeepStream application to demonstrate a human pose estimation pipeline

  •    C++

Human pose estimation is the computer vision task of estimating the configuration (‘the pose’) of the human body by localizing certain key points on a body within a video or a photo. The following application serves as a reference to deploy custom pose estimation models with DeepStream 5.0 using the TRTPose project as an example. A detailed deep-dive NVIDIA Developer blog is available here.

ros2_trt_pose - ROS 2 package for "trt_pose": real-time human pose estimation on NVIDIA Jetson Platform

  •    Python

In this repository, we build ros2 wrapper for trt_pose for real-time pose estimation on NVIDIA Jetson.

human36m_preprocessing - This instruction will help you to pre-process the Human3.6M dataset

  •    Python

This instruction will help you to pre-process the Human3.6M dataset. The source code is referred to h36m-fetch repository. However, I used OpenCV to extract images from videos instead of ffmpeg. This leads to a better quality of extracted images (3 times).






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