Update README.md

README.md

@@ -1,72 +1,46 @@
 # Learning 3D Human Dynamics from Video
 
-Angjoo Kanazawa*, Jason Zhang*, Panna Felsen*, Jitendra Malik
+This project is a modified version from the original project [Project Page](https://akanazawa.github.io/human_dynamics/). Angjoo Kanazawa*, Jason Zhang*, Panna Felsen*, Jitendra Malik, University of California, Berkeley.
 
-University of California, Berkeley
-(* Equal contribution)
-
-[Project Page](https://akanazawa.github.io/human_dynamics/)
+This fork includes the input of 2D files from OpenPose as external system. Make sure you have the AlphaPose and OpenPose installed and update the path to them.
 
 ![Teaser Image](resources/overview.jpg)
 
-### Requirements
-- Python 3 (tested on version 3.5)
-- [TensorFlow](https://www.tensorflow.org/) (tested on version 1.8)
+### Requirements Updated
+
+- Python 3 (tested on version 3.6)
+- [TensorFlow](https://www.tensorflow.org/) (tested on version 2.0)
 - [PyTorch](https://pytorch.org/) for AlphaPose, PoseFlow, and NMR (tested on
-  version 0.4.0)
+  version 1.1.3)
 - [AlphaPose/PoseFlow](https://github.com/akanazawa/AlphaPose)
 - [Neural Mesh Renderer](https://github.com/daniilidis-group/neural_renderer)
   for rendering results. See below.
-- [CUDA](https://developer.nvidia.com/cuda-downloads) (tested on CUDA 9.0 with Titan 1080 TI)
-- ffmpeg (tested on version 3.4.4)
+- [CUDA](https://developer.nvidia.com/cuda-downloads) (tested on CUDA 11.2 with GeForce 940MX)
+- ffmpeg (tested on version 4.1.3)
 
 There is currently no CPU-only support.
 
 ### License
 Please note that while our code is under BSD, the SMPL model and datasets we use have their own licenses that must be followed.
 
-### Contributions
-- Windows build and Unity port. Thanks George @ZjuSxh! https://github.com/Zju-George/human_dynamics
-
 ### Installation
 
-#### Setup virtualenv
-```
-virtualenv venv_hmmr -p python3
-source venv_hmmr/bin/activate
-pip install -U pip
-pip install numpy  # Some of the required packages need numpy to already be installed.
-deactivate
-source venv_hmmr/bin/activate
-pip install -r requirements.txt
-```
+Tested in Conda environment with python 3.6
 
+Follow all the instruction to install the original project:
 
 #### Install External Dependencies.
 Neural Mesh Renderer and AlphaPose for rendering results:
 ```
 cd src/external
 sh install_external.sh
 ```
-
-The above script also clones my fork of [AlphaPose/PoseFlow](https://github.com/akanazawa/AlphaPose),
-which is necessary to run the demo to extract tracks of people in videos. Please
-follow the directions in [the installation](https://github.com/akanazawa/AlphaPose/tree/pytorch#installation),
-in particular running `pip install -r requirements.txt` from
-`src/external/AlphaPose` and downloading the trained models.
-
-If you have a pre-installed version of AlphaPose, symlink the directory in
-`src/external`. 
-The only change that my fork has is a very minor modification in
-AlphaPose/pytorch branch's `demo.py`: see [this commit](https://github.com/akanazawa/AlphaPose/commit/ed9cd3c458f1e61145c1b10f87bd37cba53233cd),
-copy over the changes in `demo.py`. 
-
+Install the latest version of [AlphaPose] (https://github.com/MVIG-SJTU/AlphaPose), and [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
 
 ### Demo
 
 1. Download the pre-trained models (also available on [Google Drive](https://drive.google.com/file/d/1LlF9Nci8OtkqKfGwHLh7wWx1xRs7oSyF/view)). Place the `models` folder as a top-level
 directory.
-
 ```
 wget http://angjookanazawa.com/cachedir/hmmr/hmmr_models.tar.gz && tar -xf hmmr_models.tar.gz
 ```
@@ -76,17 +50,14 @@ directory.
 ```
 wget http://angjookanazawa.com/cachedir/hmmr/hmmr_demo_data.tar.gz && tar -xf hmmr_demo_data.tar.gz
 ```
-
-3. Run the demo. This code runs AlphaPose/PoseFlow for you.
-Please make sure AlphaPose can be run on a directory of images if you are having 
-any issues. 
-
 Sample usage:
 
 ```
 # Run on a single video:
 python -m demo_video --vid_path demo_data/penn_action-2278.mp4 --load_path models/hmmr_model.ckpt-1119816
 
+python -m demo_video_openpose --vid_path demo_data/penn_action-2278.mp4 --load_path models/hmmr_model.ckpt-1119816
+
 # If there are multiple people in the video, you can also pass a track index:
 python -m demo_video --track_id 1 --vid_path demo_data/insta_variety-tabletennis_43078913_895055920883203_6720141320083472384_n_short.mp4 --load_path models/hmmr_model.ckpt-1119816