diff --git a/.gitignore b/.gitignore
index dd8afab..10bb292 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 **/*.pyc
 deepspeech/deepspeech-0.9.2-checkpoint/
 **/*.wav
+!example/example.wav
 **/*.zip
 **/*.m4a
 **/*.mp4
diff --git a/README.md b/README.md
index ab25544..803da49 100644
--- a/README.md
+++ b/README.md
@@ -22,10 +22,12 @@ conda activate python36
   - Download the Basel Face Model. Due to the license agreement of Basel Face Model, you have to download the BFM09 model after submitting an application on its [home page](https://faces.dmi.unibas.ch/bfm/main.php?nav=1-2&id=downloads). After getting the access to BFM data, download "01_MorphableModel.mat" and put it into ./deep_3drecon/BFM subfolder.
   - Download Download the Expression Basis provided by [Guo et al](https://github.com/Juyong/3DFace). You can find a link named "CoarseData" in the first row of Introduction part in their repository. Download and unzip the Coarse_Dataset.zip. Put "Exp_Pca.bin" into ./deep_3drecon/BFM subfolder. The expression basis are constructed using [Facewarehouse](http://kunzhou.net/zjugaps/facewarehouse/) data and transferred to BFM topology.
   Download the pre-trained [reconstruction network](https://drive.google.com/file/d/176LCdUDxAj7T2awQ5knPMPawq5Q2RUWM/view), unzip it and put "FaceReconModel.pb" into ./deep_3drecon/network subfolder.
+  - Run `git lfs checkout ./deep_3drecon/BFM/BFM_model_front.mat`
 - Download the pretrained [audio2motion model](https://cloud.tsinghua.edu.cn/f/acb6d482a26e4eb8b116/?dl=1), put it into `./audio2motion/model`
 - Download the pretrained [texture encoder](https://cloud.tsinghua.edu.cn/f/c60a3466016948c48951/?dl=1) and [render](https://cloud.tsinghua.edu.cn/f/106023055772444f8f15/?dl=1), put it into `./render/model`
 
 #### Run
+To run our demo, you need at least one GPU with 11G GPU memory.
 ```
 python demo.py --in_img [*.png] --in_audio [*.wav] --output_path [path]
 ```
@@ -53,6 +55,8 @@ def get_style_code(exp, pose):
   return np.concatenate((exp_std, diff_exp_std, diff_pose_std))
 ```
 
+Notice that the pose of each talking face is static in current demo, you can control the pose of face by modifying the coeff_array in demo.py in line 93. The coeff_array has shape of $N * 257$ , where $N$ is framesize, vector of $257$ dimensions has same definition as [deep 3d face reconstruction](https://github.com/microsoft/Deep3DFaceReconstruction), where $254-257$ dim controls the translation, and $224-227$ dim controls euler angles for pose.
+
 ------
 
 ### Project Overview
diff --git a/demo.py b/demo.py
index e69de29..ba13da1 100644
--- a/demo.py
+++ b/demo.py
@@ -0,0 +1,249 @@
+import deep_3drecon
+import face_alignment
+import cv2
+import os
+import numpy as np
+import librosa
+import torch
+import math
+import audio2motion
+import render
+import torch.nn as nn
+import torchvision
+import argparse
+from align_img import align_lm68, align
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--in_img", help = "input portrait", default='example/example.png', type=str)
+parser.add_argument("--in_audio", help="input audio", default='example/example.wav', type=str)
+parser.add_argument("--output_path", help="output path of videos", default='output', type=str)
+conf = parser.parse_args()
+
+def lm68_2_lm5(in_lm):
+    lm_idx = np.array([31,37,40,43,46,49,55]) - 1
+    lm = np.stack([in_lm[lm_idx[0],:],np.mean(in_lm[lm_idx[[1,2]],:],0),np.mean(in_lm[lm_idx[[3,4]],:],0),in_lm[lm_idx[5],:],in_lm[lm_idx[6],:]], axis = 0)
+    lm = lm[[1,2,0,3,4],:2]
+    return lm
+
+def recon_texture(uv_file, img_file, out_path):
+    uv_img = cv2.imread(uv_file).astype(np.int32)
+    img = cv2.imread(img_file).astype(np.int32)
+    
+    x = uv_img[:, :, 0].reshape(-1)
+    y = uv_img[:, :, 1].reshape(-1)
+    index = y * 256 + x
+    img = img.reshape(-1, 3)
+    texture = np.zeros((256 * 256, 3), dtype = np.int32)
+    texture_count = np.zeros((256 * 256), dtype = np.int32)
+    
+    np.add.at(texture_count, index, 1)
+    np.add.at(texture, index, img)
+    texture_count[texture_count == 0] = 1
+    texture = texture / np.expand_dims(texture_count, 1)
+
+    texture = texture.reshape(256, 256, 3)
+    cv2.imwrite(out_path, texture)
+
+
+def read_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frame_list = []
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if ret == False:
+            break
+        frame_list.append(frame)
+    return np.array(frame_list)
+
+os.environ["CUDA_VISIBLE_DEVICES"]=str(0)
+img_path = conf.in_img
+audio_path = conf.in_audio
+tmp_path = conf.output_path
+
+if len(os.listdir(tmp_path)) != 0:
+    raise Exception("Output path must be empty")
+
+fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, network_size=4, device='cuda')
+face_reconstructor = deep_3drecon.Reconstructor()
+im_bgr = cv2.imread(img_path)
+im_rgb = im_bgr[:, :, ::-1]
+lm68 = fa.get_landmarks(im_rgb)[0]
+lm5 = lm68_2_lm5(lm68)
+coeff, align_img = face_reconstructor.recon_coeff(np.array([im_bgr]), np.array([lm5]), return_image = True)
+# Due to one bug, we use tex_noise.png in our implementation, currently the tex_no_noise.mp4 is not used, which will be fixed further.
+face_reconstructor.recon_texture_from_coeff(coeff, align_img, os.path.join(tmp_path, "tex_no_noise.mp4"), tmp_dir = tmp_path)
+face_reconstructor.recon_uv_from_coeff(coeff, os.path.join(tmp_path, "uv_src.mp4"), tmp_dir = tmp_path)
+
+align_img = align_img[0]
+cv2.imwrite(os.path.join(tmp_path, "align.png"), align_img)
+uv_img = read_video(os.path.join(tmp_path, "uv_src.mp4"))[0]
+recon_texture(os.path.join(tmp_path, "0.png"), os.path.join(tmp_path, "align.png"), os.path.join(tmp_path, "tex_noise.png"))
+tex_img = cv2.imread(os.path.join(tmp_path, "tex_noise.png")).astype(np.int32)
+
+del fa
+torch.cuda.empty_cache()
+
+import deepspeech
+deepspeech_prob = deepspeech.get_prob(audio_path)
+del deepspeech
+audio, sr = librosa.load(audio_path, sr = 16000)
+audio_energy = librosa.feature.rms(y = audio, frame_length = 512, hop_length = 320, center = False)
+audio_energy = np.transpose(audio_energy)
+coeff_len = len(deepspeech_prob) // 2
+coeff_array = np.tile(coeff, (coeff_len, 1))
+
+y_len = len(coeff_array)
+audio_clip_list = []
+energy_clip_list = []
+
+def get_sync_data(x, x_dim, y_left, y_right):
+    x_len = len(x)
+    x_left = math.floor(y_left * 50 / 25)
+    x_right = math.floor(y_right * 50 / 25)
+    pad_len = 80 - x_right + x_left
+
+    if pad_len % 2 == 0:
+        pad_left = pad_len // 2
+        pad_right = pad_len // 2
+    else:
+        pad_left = pad_len // 2
+        pad_right = pad_len - pad_left
+
+    x_left = x_left - pad_left
+    x_right = x_right + pad_right
+    if x_left < 0:
+        if x_right > x_len:
+            x_data = np.concatenate((np.tile(x[0], -1 * x_left).reshape(-1, x_dim), x, np.tile(x[-1], x_right - x_len).reshape(-1, x_dim)), axis = 0)
+        else:
+            x_data = x[0: x_right]
+            x_data = np.concatenate((np.tile(x[0], -1 * x_left).reshape(-1, x_dim), x_data), axis = 0)
+    elif x_right > x_len:
+        x_data = x[x_left: x_len]
+        x_data = np.concatenate((x_data, np.tile(x[-1], x_right - x_len).reshape(-1, x_dim)), axis = 0)
+    else:
+        x_data = x[x_left: x_right]
+    
+    return x_data
+
+for i in range(0, y_len, 8):
+    if i > y_len - 32:
+        y_left = y_len - 32
+        y_right = y_len
+    else:
+        y_left = i
+        y_right = i + 32
+
+    audio_clip = get_sync_data(deepspeech_prob, 29, y_left, y_right)
+    energy_clip = get_sync_data(audio_energy, 1, y_left, y_right)
+
+    audio_clip_list.append(audio_clip)
+    energy_clip_list.append(energy_clip)
+
+audio_clip_list = np.array(audio_clip_list)
+energy_clip_list = np.array(energy_clip_list)
+
+audio_batch = torch.from_numpy(audio_clip_list).transpose(1, 2).float().cuda()
+energy_batch = torch.from_numpy(energy_clip_list).transpose(1, 2).float().cuda()
+sty_batch = torch.from_numpy(np.load("./example/sty.npy")).float().cuda()
+
+torch.cuda.empty_cache()
+
+# os.environ["CUDA_VISIBLE_DEVICES"]=str(1)
+model = audio2motion.StyleFusionModel().cuda()
+model.load_state_dict(torch.load("./audio2motion/model/backbone.pkl"), strict = True)
+model.eval()
+
+with torch.no_grad():
+    for idx in range(10):
+        sty_tmp = sty_batch[idx].unsqueeze(0).repeat(len(audio_batch), 1)
+        pred_exp_batch = model(audio_batch, energy_batch, sty_tmp)
+
+        y_len = len(coeff_array)
+        y_repeat = torch.zeros(y_len).int().cuda()
+        predict_exp_cat = torch.zeros((y_len, 64)).float().cuda()
+        for counter, i in enumerate(range(0, y_len, 8)):
+            if i > y_len - 32:
+                y_left = y_len - 32
+                y_right = y_len
+            else:
+                y_left = i
+                y_right = i + 32
+            y_repeat[y_left: y_right] += 1
+            predict_exp_cat[y_left: y_right] += pred_exp_batch[counter].transpose(0, 1)
+        y_repeat = y_repeat.float()
+        predict_exp_cat = predict_exp_cat / y_repeat.unsqueeze(1)
+        coeff_array[:, 80:144] = predict_exp_cat.detach().cpu().numpy()
+        np.save(os.path.join(tmp_path, "test_{}.npy".format(idx)), coeff_array)
+        face_reconstructor.recon_uv_from_coeff(coeff_array, 
+            out_path = os.path.join(tmp_path, "test_uv_{}.mp4".format(idx)), 
+            bg_path = os.path.join(tmp_path, "test_bg_{}.mp4".format(idx)), 
+            tmp_dir = tmp_path
+        )
+
+del model
+del face_reconstructor
+del audio_batch
+del energy_batch
+del sty_batch
+del coeff_array
+del predict_exp_cat
+torch.cuda.empty_cache()
+
+tex_encoder = render.UNet(9, 16).cuda()
+tex_sampler = render.TexSampler().cuda()
+face_unet = render.define_G(16, 3, 64, 'local').cuda()
+tex_encoder.load_state_dict(torch.load("./render/model/tex_encoder.pkl"), strict = True)
+face_unet.load_state_dict(torch.load("./render/model/face_unet.pkl"), strict = True)
+tex_encoder.eval()
+tex_sampler.eval()
+face_unet.eval()
+tex_img = torch.from_numpy(tex_img).float().permute(2, 0, 1) / 128 - 1
+tex_img_batch = tex_img.unsqueeze(0).repeat(3, 1, 1, 1).cuda()
+
+with torch.no_grad():
+    batch_size = 8
+    for idx in range(10):
+        uv_path = os.path.join(tmp_path, "test_uv_{}.mp4".format(idx))
+        bg_path = os.path.join(tmp_path, "test_bg_{}.mp4".format(idx))
+        bg_frames = np.array(read_video(bg_path))
+        uv_frames = np.array(read_video(uv_path))
+        uv_frames = torch.from_numpy(uv_frames).float().permute(0, 3, 1, 2) / 255
+        uv_img_batch = uv_frames[:, :2]
+        bg_img_batch = torch.from_numpy(bg_frames > 127).float()[:, :, :, 0].unsqueeze(1)
+        bg_img_batch, uv_img_batch, tex_img_batch = bg_img_batch.unsqueeze(0), uv_img_batch.unsqueeze(0), tex_img_batch.unsqueeze(0)
+        tex_img_batch = tex_img_batch.reshape(tex_img_batch.shape[0], -1, tex_img_batch.shape[3], tex_img_batch.shape[4]).cuda()
+        tex = tex_encoder(tex_img_batch)
+        pred_img_batch = torch.zeros((uv_img_batch.shape[0], uv_img_batch.shape[1], 3, uv_img_batch.shape[3], uv_img_batch.shape[4])).float()
+        start_idx = 0
+        while start_idx < pred_img_batch.shape[1]:
+            if start_idx + batch_size > pred_img_batch.shape[1]:
+                end_idx = pred_img_batch.shape[1]
+            else:
+                end_idx = start_idx + batch_size
+            bg_tmp_batch = bg_img_batch[:, start_idx: end_idx].cuda()
+            uv_tmp_batch = uv_img_batch[:, start_idx: end_idx].cuda()
+            bg_tmp_batch = bg_tmp_batch.reshape(-1, bg_tmp_batch.shape[2], bg_tmp_batch.shape[3], bg_tmp_batch.shape[4])
+            uv_tmp_batch = uv_tmp_batch.reshape(-1, uv_tmp_batch.shape[2], uv_tmp_batch.shape[3], uv_tmp_batch.shape[4])
+            tex_tmp = tex.unsqueeze(1).repeat(1, uv_tmp_batch.shape[0], 1, 1, 1)
+            tex_tmp = tex_tmp.reshape(-1, tex_tmp.shape[2], tex_tmp.shape[3], tex_tmp.shape[4])
+            sample_image = tex_sampler(uv_tmp_batch, tex_tmp)
+            pred_image = face_unet(sample_image) * (1 - bg_tmp_batch)
+            pred_img_batch[:, start_idx:end_idx] = pred_image.cpu()
+            start_idx += batch_size
+        pred_img_batch = pred_img_batch[0].cpu().detach()
+        pred_img_batch = torch.flip(pred_img_batch, dims = [1])
+        os.system("rm {}".format(os.path.join(tmp_path, "*.png")))
+        for i in range(len(pred_img_batch)):
+            torchvision.utils.save_image(pred_img_batch[i], "./{}/{}.png".format(tmp_path, i), normalize = True, range = (-1, 1))
+        os.system("ffmpeg -y -loglevel warning -framerate 25 -start_number 0 -i {}/%d.png -c:v libx264 -pix_fmt yuv420p -b:v 2000k {}/render_{}.mp4".format(tmp_path, tmp_path, idx))
+        os.system("ffmpeg -y -loglevel warning -i {} -i {} -map 0:v -map 1:a -c:v copy -shortest {}".format(
+            "{}/render_{}.mp4".format(tmp_path, idx),
+            audio_path,
+            "{}/result_{}.mp4".format(tmp_path, idx),
+        ))
+
+os.system("rm {}".format(os.path.join(tmp_path, "*.png")))
+os.system("rm {}".format(os.path.join(tmp_path, "test_*")))
+os.system("rm {}".format(os.path.join(tmp_path, "render_*")))
+os.system("rm {}".format(os.path.join(tmp_path, "tex_no_noise.mp4")))
+os.system("rm {}".format(os.path.join(tmp_path, "uv_src.mp4")))
\ No newline at end of file
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