Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019
| Date | Stars |
|---|---|
| 2026-07-24 | 864 |
| 2026-07-25 | 864 |
| 2026-07-28 | 864 |
| 2026-07-30 | 864 |
| 2026-08-06 | 864 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# PoseNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image"
<p align="center">
<img src="assets/qualitative_intro.PNG" width="800" height="300">
</p>
<p align="middle">
<img src="assets/posetrack_1.gif" width="200" height="150"> <img src="assets/posetrack_2.gif" width="200" height="150"><img src="assets/posetrack_3.gif" width="200" height="150"> <img src="assets/posetrack_4.gif" width="200" height="150">
</p>
## Introduction
This repo is official **[PyTorch](https://pytorch.org)** implementation of **[Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image (ICCV 2019)](https://arxiv.org/abs/1907.11346)**. It contains **PoseNet** part.
**What this repo provides:**
* [PyTorch](https://pytorch.org) implementation of [Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image (ICCV 2019)](https://arxiv.org/abs/1907.11346).
* Flexible and simple code.
* Compatibility for most of the publicly available 2D and 3D, single and multi-person pose estimation datasets including **[Human3.6M](http://vision.imar.ro/human3.6m/description.php), [MPII](http://human-pose.mpi-inf.mpg.de/), [MS COCO 2017](http://cocodataset.org/#home), [MuCo-3DHP](http://gvv.mpi-inf.mpg.de/projects/SingleShotMultiPerson/) and [MuPoTS-3D](http://gvv.mpi-inf.mpg.de/projects/SingleShotMultiPerson/)**.
* Human pose estimation visualization code.
## Dependencies
* [PyTorch](https://pytorch.org)
* [CUDA](https://developer.nvidia.com/cuda-downloads)
* [cuDNN](https://developer.nvidia.com/cudnn)
* [Anaconda](https://www.anaconda.com/download/)
* [COCO API](https://github.com/cocodataset/cocoapi)
This code is tested under Ubuntu 16.04, CUDA 9.0, cuDNN 7.1 environment with two NVIDIA 1080Ti GPUs.
Python 3.6.5 version with Anaconda 3 is used for development.
## Quick demo
You can try quick demo at `demo` folder.
* Download the pre-trained PoseNet in [here](https://drive.google.com/file/d/1xjefKgjsH-W8v2lVn1Hbwzp2_Su7KtG0/view?usp=sharing).
* Prepare `input.jpg` and pre-trained snapshot at `demo` folder.
* Set `bbox_list` at [here](https://github.com/mks0601/3DMPPE_POSENET_RELEASE/blob/f8d420895570682c815d37ea74e2d6b1bf646818/demo/demo.py#L68).
* Set `root_depth_list` at [here](https://github.com/mks0601/3DMPPE_POSENET_RELEASE/blob/bf4fa6f8076fcd3f636ddcc31479e2becbba19ec/demo/demo.py#L74).
* Run `python demo.py --gpu 0 --test_epoch 24` if you want to run on gpu 0.
* You can see `output_pose_2d.jpg` and new window that shows 3D pose.
## Directory
### Root
The `${POSE_ROOT}` is described as below.
```
${POSE_ROOT}
|-- data
|-- demo
|-- common
|-- main
|-- tool
|-- vis
`-- output
```
* `data` contains data loading codes and soft links to images and annotations directories.
* `demo` contains demo codes.
* `common` contains kernel codes for 3d multi-person pose estimation system.
* `main` contains high-level codes for training or testing the network.
* `tool` contains data pre-processing codes. You don't have to run this code. I provide pre-processed data below.
* `vis` contains scripts for 3d visualization.
* `output` contains log, trained models, visualized outputs, and test result.
### Data
You need to follow directory structure of the `data` as below.
```
${POSE_ROOT}
|-- data
| |-- Human36M
| | |-- bbox_root
| | | |-- bbox_root_human36m_output.json
| | |-- images
| | |-- annotations
| |-- MPII
| | |-- images
| | |-- annotations
| |-- MSCOCO
| | |-- bbox_root
| | | |-- bbox_root_coco_output.json
| | |-- images
| | | |-- train2017
| | | |-- val2017
| | |-- annotations
| |-- MuCo
| | |-- data
| | | |-- augmented_set
| | | |-- unaugmented_set
| | | |-- MuCo-3DHP.json
| |-- MuPoTS
| | |-- bbox_root
| | | |-- bbox_mupots_output.json
| | |-- data
| | | |-- MultiPersonTestSet
| | | |-- MuPoTS-3D.json
```
* Download HumaExcerpt of 8,854 characters
Read on GitHub
89
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:90044fdcecee9a2e, topic:deep-learning, topic:pytorch
matched fp:90044fdcecee9a2e, topic:computer-vision, desc:pose estimation, readme:pose estimation