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README.md

@@ -96,9 +96,30 @@ We experimented on two skeleton-based action recognition datasts: **Kinetics-ske
 Before training and testing, for convenience of fast data loading,
 the datasets should be converted to proper file structure. 
 You can download the pre-processed data from 
-[GoogleDrive](https://drive.google.com/open?id=103NOL9YYZSW1hLoWmYnv5Fs8mK-Ij7qb).
-Otherwise, check [data_preparation.md](data_preparation.md)
-for more details about two datasets and instructions on how to process raw data.
+[GoogleDrive](https://drive.google.com/open?id=103NOL9YYZSW1hLoWmYnv5Fs8mK-Ij7qb)
+and extract files with
+``` 
+cd st-gcn
+unzip <path to st-gcn-processed-data.zip>
+```
+Otherwise, for processing raw data by yourself,
+please refer to below guidances.
+
+### Kinetics-skeleton
+[Kinetics](https://deepmind.com/research/open-source/open-source-datasets/kinetics/) is a video-based dataset for action recognition which only provide raw video clips without skeleton data. Kinetics dataset include To obatin the joint locations, we first resized all videos to the resolution of 340x256 and converted the frame rate to 30 fps.  Then, we extracted skeletons from each frame in Kinetics by [Openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose). The extracted skeleton data we called **Kinetics-skeleton**(7.5GB) can be directly downloaded from [GoogleDrive](https://drive.google.com/open?id=1SPQ6FmFsjGg3f59uCWfdUWI-5HJM_YhZ) or [BaiduYun](https://pan.baidu.com/s/1dwKG2TLvG-R1qeIiE4MjeA#list/path=%2FShare%2FAAAI18%2Fkinetics-skeleton&parentPath=%2FShare).
+
+After uncompressing, rebuild the database by this command:
+```
+python tools/kinetics_gendata.py --data_path <path to kinetics-skeleton>
+```
+
+### NTU RGB+D
+NTU RGB+D can be downloaded from [their website](http://rose1.ntu.edu.sg/datasets/actionrecognition.asp).
+Only the **3D skeletons**(5.8GB) modality is required in our experiments. After that, this command should be used to build the database for training or evaluation:
+```
+python tools/ntu_gendata.py --data_path <path to nturgbd+d_skeletons>
+```
+where the ```<path to nturgbd+d_skeletons>``` points to the 3D skeletons modality of NTU RGB+D dataset you download.
 
 
 ## Testing Pretrained Models