diff --git a/demo/video_matting/offline/requirements.txt b/demo/video_matting/offline/requirements.txt
new file mode 100644
index 0000000..44c4e44
--- /dev/null
+++ b/demo/video_matting/offline/requirements.txt
@@ -0,0 +1,6 @@
+numpy
+Pillow
+opencv-python
+torch >= 1.0.0
+torchvision
+tqdm
\ No newline at end of file
diff --git a/demo/video_matting/offline/run.py b/demo/video_matting/offline/run.py
new file mode 100644
index 0000000..e2d18ea
--- /dev/null
+++ b/demo/video_matting/offline/run.py
@@ -0,0 +1,106 @@
+import os
+
+import cv2
+import numpy as np
+from PIL import Image
+import argparse
+from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+
+from src.models.modnet import MODNet
+
+
+torch_transforms = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+    ]
+)
+
+print('Load pre-trained MODNet...')
+pretrained_ckpt = './pretrained/modnet_webcam_portrait_matting.ckpt'
+modnet = MODNet(backbone_pretrained=False)
+modnet = nn.DataParallel(modnet)
+
+GPU = True if torch.cuda.device_count() > 0 else False
+if GPU:
+    print('Use GPU...')
+    modnet = modnet.cuda()
+    modnet.load_state_dict(torch.load(pretrained_ckpt))
+else:
+    print('Use CPU...')
+    modnet.load_state_dict(torch.load(pretrained_ckpt, map_location=torch.device('cpu')))
+modnet.eval()
+
+
+def offline_matting(video_path, save_path, alpha_matte=False, fps=30):
+    # video capture
+    vc = cv2.VideoCapture(video_path)
+
+    if vc.isOpened():
+        rval, frame = vc.read()
+    else:
+        rval = False
+
+    if not rval:
+        print('Read video {} failed.'.format(video_path))
+        exit()
+
+    num_frame = vc.get(cv2.CAP_PROP_FRAME_COUNT)
+    h, w = frame.shape[:2]
+
+    # video writer
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    video_writer = cv2.VideoWriter(save_path, fourcc, fps, (w, h))
+
+    print('Start matting...')
+    with tqdm(range(int(num_frame)))as t:
+        for c in t:
+            frame_np = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame_np = cv2.resize(frame_np, (672, 512), cv2.INTER_AREA)
+
+            frame_PIL = Image.fromarray(frame_np)
+            frame_tensor = torch_transforms(frame_PIL)
+            frame_tensor = frame_tensor[None, :, :, :]
+            if GPU:
+                frame_tensor = frame_tensor.cuda()
+
+            with torch.no_grad():
+                _, _, matte_tensor = modnet(frame_tensor, True)
+
+            matte_tensor = matte_tensor.repeat(1, 3, 1, 1)
+            matte_np = matte_tensor[0].data.cpu().numpy().transpose(1, 2, 0)
+            if alpha_matte:
+                view_np = matte_np * np.full(frame_np.shape, 255.0)
+            else:
+                view_np = matte_np * frame_np + (1 - matte_np) * np.full(frame_np.shape, 255.0)
+            view_np = cv2.cvtColor(view_np.astype(np.uint8), cv2.COLOR_RGB2BGR)
+            view_np = cv2.resize(view_np, (w, h))
+            video_writer.write(view_np)
+
+            rval, frame = vc.read()
+            c += 1
+
+    video_writer.release()
+    print('Save video to {}'.format(save_path))
+    return
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--video_path', type=str, default='./sample/video.mp4')
+    parser.add_argument('--save_path', type=str, default='./sample/res.mp4', help='Video should be .mp4 format.')
+    parser.add_argument('--alpha_matte', action='store_true', default=False, help='If True, save alpha_matte video.')
+    parser.add_argument('--fps', type=int, default=30)
+
+    args = parser.parse_args()
+
+    if not args.save_path.endswith('.mp4'):
+        args.save_path = os.path.splitext(args.save_path)[0] + '.mp4'
+
+    offline_matting(args.video_path, args.save_path, args.alpha_matte, args.fps)
+
+