diff --git a/.gitignore b/.gitignore
index 401af08d6..5201a6afa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,7 +8,6 @@ backup/*
 model/*
 *.pt
 *.caffemodel
-resource/media
 cache/
 
 # Byte-compiled / optimized / DLL files
diff --git a/README.md b/README.md
index 20378923d..18c11f376 100644
--- a/README.md
+++ b/README.md
@@ -87,11 +87,10 @@ To visualize how ST-GCN exploit local correlation and local pattern, we compute
 
 Run the demo by this command:
 ```
-python main.py demo --openpose <path to openpose build directory> [--video <path to your video>]
+python main.py demo --openpose <path to openpose build directory> [--video <path to your video> --device <gpu0> <gpu1>]
 ```
 A video as above will be generated.
 
-
 ## Data Preparation
 
 We experimented on two skeleton-based action recognition datasts: **Kinetics-skeleton** and **NTU RGB+D**. The experiments on NTU RGB+D
diff --git a/resource/media/BQkGMJD39c8.mp4 b/resource/media/BQkGMJD39c8.mp4
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index 40e9be5f8..000000000
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diff --git a/resource/media/skateboarding.mp4 b/resource/media/skateboarding.mp4
new file mode 100644
index 000000000..7835a51a6
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diff --git a/resource/media/ta_chi.mp4 b/resource/media/ta_chi.mp4
new file mode 100644
index 000000000..8c0a62aef
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diff --git a/resource/media/zZ9Zb41KNR4.mp4 b/resource/media/zZ9Zb41KNR4.mp4
deleted file mode 100644
index 2a4fc289d..000000000
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diff --git a/tools/utils/openpose.py b/tools/utils/openpose.py
index 10f5979e8..00d634764 100644
--- a/tools/utils/openpose.py
+++ b/tools/utils/openpose.py
@@ -6,7 +6,8 @@ def json_pack(snippets_dir, video_name, frame_width, frame_height, label='unknow
     p = Path(snippets_dir)
     for path in p.glob(video_name+'*.json'):
         json_path = str(path)
-        frame_id = int(path.stem.split('_')[1])
+        print(path)
+        frame_id = int(path.stem.split('_')[-2])
         frame_data = {'frame_index': frame_id}
         data = json.load(open(json_path))
         skeletons = []