diff --git a/README.md b/README.md
index c132d81..4f483f2 100644
--- a/README.md
+++ b/README.md
@@ -119,7 +119,7 @@ We strongly recommend installing both PyTorch and TorchVision with CUDA support,
 git clone https://github.com/facebookresearch/co-tracker
 cd co-tracker
 pip install -e .
-pip install matplotlib flow_vis tqdm tensorboard
+pip install matplotlib flow_vis tqdm tensorboard imageio[ffmpeg]
 ```
 
 You can manually download the CoTracker2 checkpoint from the links below and place it in the `checkpoints` folder as follows:
@@ -132,6 +132,11 @@ cd ..
 ```
 For old checkpoints, see [this section](#previous-version).
 
+After installation, this is how you could run the model on `./assets/apple.mp4` (results will be saved to `./saved_videos/apple.mp4`):
+```bash
+python demo.py --checkpoint checkpoints/cotracker2.pth
+```
+
 ## Evaluation
 
 To reproduce the results presented in the paper, download the following datasets:
diff --git a/demo.py b/demo.py
index 94a87f7..2f26587 100644
--- a/demo.py
+++ b/demo.py
@@ -83,11 +83,12 @@ if __name__ == "__main__":
     print("computed")
 
     # save a video with predicted tracks
-    seq_name = args.video_path.split("/")[-1]
+    seq_name = os.path.splitext(args.video_path.split("/")[-1])[0]
     vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3)
     vis.visualize(
         video,
         pred_tracks,
         pred_visibility,
         query_frame=0 if args.backward_tracking else args.grid_query_frame,
+        filename=seq_name,
     )
diff --git a/online_demo.py b/online_demo.py
index 7aad145..d1f4321 100644
--- a/online_demo.py
+++ b/online_demo.py
@@ -97,7 +97,7 @@ if __name__ == "__main__":
     print("Tracks are computed")
 
     # save a video with predicted tracks
-    seq_name = args.video_path.split("/")[-1]
+    seq_name = os.path.splitext(args.video_path.split("/")[-1])[0]
     video = torch.tensor(np.stack(window_frames), device=DEFAULT_DEVICE).permute(0, 3, 1, 2)[None]
     vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3)
-    vis.visualize(video, pred_tracks, pred_visibility, query_frame=args.grid_query_frame)
+    vis.visualize(video, pred_tracks, pred_visibility, query_frame=args.grid_query_frame, filename=seq_name)