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docs/en_US/tutorials/motion_driving.md

-# First Order Motion model
+# First Order Motion
 
-## First Order Motion model introduction
-
-[First order motion model](https://arxiv.org/abs/2003.00196) is to complete the Image animation task, which consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. The first order motion framework addresses this problem without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objects of the same category (e.g. faces, human bodies), this method can be applied to any object of this class. To achieve this, the innovative method decouple appearance and motion information using a self-supervised formulation. In addition, to support complex motions, it use a representation consisting of a set of learned keypoints along with their local affine transformations. A generator network models occlusions arising during target motions and combines the appearance extracted from the source image and the motion derived from the driving video.
+[First order motion model](https://arxiv.org/abs/2003.00196) is to complete the Image animation task, which consists of generating a video sequence so that an object in a source image is animated according to the motion of the driving video. The image below gives examples of source images with objects and driving videos containing a series of motions.
 
 <div align="center">
   <img src="../../imgs/fom_demo.png" width="500"/>
 </div>
-## Multi-Faces swapping
+Taking the face in the upper left corner as an example, given a source object and a driving video, a new video can be generated in which the source subject wears an expression that is derived from the driving video. Usually, we need to annotate the keypoints of the source object and train the model for facial expression transfer.
+
+The following gif clearly expounds the principle:  
+
+[![img](https://user-images.githubusercontent.com/48054808/127443878-b9369c1a-909c-4af6-8c84-a62821262910.gif)](https://user-images.githubusercontent.com/48054808/127443878-b9369c1a-909c-4af6-8c84-a62821262910.gif)
+
+The proposed method is not exclusively for facial expression transfer, it also supports other object with training on similar datasets. For example, you can transfer the motion of playing Tai Chi by training on Tai Chi video datasets and achieve facial expression transfer by using dataset voxceleb for training. After that, you could realize real-time image animation with the corresponding pre-training model.
+
+## Features
+
+- #### Multi-Faces Swapping
+
+  - **Unique face detection algorithm that supports automatic multi-faces detection and expression swapping.**
+
+    - We adopt PaddleGAN's face detection model [S3FD](https://github.com/PaddlePaddle/PaddleGAN/tree/develop/ppgan/faceutils/face_detection/detection) to detect all the faces in an image and transfer those expressions for multi-faces swapping.
 
-For photoes with multiple faces, we first detect all of the faces,  then do facial expression transfer for each face, and finally put those faces back to the original photo to generate a complete new video.
+      Specific technical steps are shown below:
 
-Specific technical steps are shown below:
+       a. Use the S3FD model to detect all the faces in an image
 
-1. Use the S3FD model to detect the faces of a photo
-2. Use the First Order Motion model to do the facial expression transfer of each face
-3. Put those "new" generated faces back to the original photo
+       b. Use the First Order Motion model to do the facial expression transfer of each face
 
-At the same time, specifically for face related work, PaddleGAN provides a ["faceutils" tool](https://github.com/PaddlePaddle/PaddleGAN/tree/develop/ppgan/faceutils), including face detection, face segmentation models and more.
+       c. Crop those "new" generated faces and put them back to the original photo
+
+    At the same time, PaddleGAN also provides a ["faceutils" tool](https://github.com/PaddlePaddle/PaddleGAN/tree/develop/ppgan/faceutils) for face-related work, including face detection, face segmentation, keypoints detection, etc.
+
+- #### Face Enhancement 
+
+  - **This effect significantly improves the definition of the driven video.**
+
+- #### Abundant applications for online experience
+
+  - 🐜**Ant Ah Hey**🐜：https://aistudio.baidu.com/aistudio/projectdetail/1603391
+  - 💙**Special For Love Confession on May 20th (pronounced as I love you)**💙：https://aistudio.baidu.com/aistudio/projectdetail/1956943
+  - **Smile of the Deceased(▰˘◡˘▰)**：https://aistudio.baidu.com/aistudio/projectdetail/1660701
+  - 👨**Special For Father's Day**：https://aistudio.baidu.com/aistudio/projectdetail/2068655
 
 ## How to use
-### 1 Test for Face
-Users can upload the prepared source image and driving video, then substitute the path of source image and driving video for the `source_image` and `driving_video` parameter in the following running command. It will geneate a video file named `result.mp4` in the `output` folder, which is the animated video file.
+### 1. Quick Start: Face Detection and Effect Enhancement
+
+Users can upload a source image with single or multiple faces and driving video, then substitute the paths of source image and driving video for the `source_image` and `driving_video` parameters respectively and run the following command. It will generate a video file named `result.mp4` in the `output` folder, which is the animated video file.
+
+Note: For photos with multiple faces, the longer the distances between faces, the better the result quality you can get, or you could optimize the effect by adjusting ratio.
+
+The original image and driving video here are provided for demonstration purposes, the running command is as follows:
 
-Note: for photoes with multiple faces, the longer the distances between faces, the better the result quality you can get.  
+#### Running Command：
 
-- single face:
 ```
 cd applications/
 python -u tools/first-order-demo.py  \
      --driving_video ../docs/imgs/fom_dv.mp4 \
      --source_image ../docs/imgs/fom_source_image.png \
      --ratio 0.4 \
-     --relative --adapt_scale \
+     --relative \
+     --adapt_scale \
      --image_size 512 \
-     --face_enhancement
+     --face_enhancement \
+     --multi_person
 ```
 
-- multi face：
-```
-cd applications/
-python -u tools/first-order-demo.py  \
-     --driving_video ../docs/imgs/fom_dv.mp4 \
-     --source_image ../docs/imgs/fom_source_image_multi_person.png \
-     --ratio 0.4 \
-     --relative --adapt_scale \
-     --image_size 512 \
-     --multi_person
 
 
-**params:**
-- driving_video: driving video, the motion of the driving video is to be migrated.
-- source_image: source_image, support single people and multi-person in the image, the image will be animated according to the motion of the driving video.
-- relative: indicate whether the relative or absolute coordinates of the key points in the video are used in the program. It is recommended to use relative coordinates. If absolute coordinates are used, the characters will be distorted after animation.
-- adapt_scale: adapt movement scale based on convex hull of keypoints.
-- ratio: The pasted face percentage of generated image, this parameter should be adjusted in the case of multi-person image in which the adjacent faces are close. The defualt value is 0.4 and the range is [0.4, 0.5].
-- image_size: The image size of the face. Default is 256
-- multi_person: There are multi faces in the images. Default means only one face in the image
-- face_enhancement: enhance the face, default is False
-```
-result of face_enhancement:
-<div align='center'>
-  <img src='https://user-images.githubusercontent.com/17897185/126444836-b68593e3-ae43-4450-b18f-1a549230bf07.gif' width='700'/>
-</div>
-<div align='center'>
-  <img src='https://user-images.githubusercontent.com/17897185/126444194-436cc885-259d-4636-ad4c-c3dcc52fe175.gif' width='700'/>
-</div>
+#### Parameters：
+
+| Parameters       | Instructions                                                 |
+| ---------------- | ------------------------------------------------------------ |
+| driving_video    | driving video, the motion and expression of the driving video is to be migrated. |
+| source_image     | source image, support single face and multi-faces images, the image will be animated according to the motion and expression of the driving video. |
+| relative         | indicate whether the relative or absolute coordinates of the key points in the video are used in the program. It is recommended to use relative coordinates, or the characters will be distorted after animation. |
+| adapt_scale      | adaptive movement scale based on convex hull of keypoints.   |
+| ratio            | The pasted face percentage of generated image, this parameter should be adjusted in the case of multi-person image in which the adjacent faces are close. The default value is 0.4 and the range is [0.4, 0.5]. |
+| image_size       | The image size of the face. 256 by default, 512 is supported |
+| face_enhancement | enhance the face, closed by default with no parameter added. |
+| multi_person     | multiple faces in the image. Default means only one face in the image |
+
+#### 📣Result of Face Enhancement
+
+|                            Before                            |                            After                             |
+| :----------------------------------------------------------: | :----------------------------------------------------------: |
+| [![img](https://user-images.githubusercontent.com/17897185/126444836-b68593e3-ae43-4450-b18f-1a549230bf07.gif)](https://user-images.githubusercontent.com/17897185/126444836-b68593e3-ae43-4450-b18f-1a549230bf07.gif) | [![img](https://user-images.githubusercontent.com/17897185/126444194-436cc885-259d-4636-ad4c-c3dcc52fe175.gif)](https://user-images.githubusercontent.com/17897185/126444194-436cc885-259d-4636-ad4c-c3dcc52fe175.gif) |
+
+### 2. Training
+
+#### **Datasets:**
 
+- fashion See [here](https://vision.cs.ubc.ca/datasets/fashion/)
+- VoxCeleb See [here](https://github.com/AliaksandrSiarohin/video-preprocessing). Here you can process the data sizes according to your requirements. We deal with two resolution sizes: 256 and 512, the results can be seen below： [![img](https://github.com/PaddlePaddle/PaddleGAN/raw/develop/docs/imgs/fom_512_vs_256.png)](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/imgs/fom_512_vs_256.png)
 
-### 2 Training
-**Datasets:**
-- fashion See[here](https://vision.cs.ubc.ca/datasets/fashion/)
-- VoxCeleb See[here](https://github.com/AliaksandrSiarohin/video-preprocessing). Here you can process the data sizes according to your requirements. We deal with two sizes: 256 and 512, the results can be seen below
-![](../../imgs/fom_512_vs_256.png)
+**Parameters:**
 
-**params:**
-- dataset_name.yaml: Create a config of your own dataset
+- dataset_name.yaml: Configure your own yaml document and parameters
 
-- For single GPU:
+- Training using single GPU:
 ```
 export CUDA_VISIBLE_DEVICES=0
 python tools/main.py --config-file configs/dataset_name.yaml
 ```
-- For multiple GPUs:
+- Training using multiple GPUs: change the *nn.BatchNorm* in /ppgan/modules/first_order.py to *nn.SyncBatchNorm*
 ```
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python -m paddle.distributed.launch \
@@ -92,12 +113,12 @@ python -m paddle.distributed.launch \
 ```
 
 **Example:**
-- For single GPU:
+- Training using single GPU:
 ```
 export CUDA_VISIBLE_DEVICES=0
 python tools/main.py --config-file configs/firstorder_fashion.yaml \
 ```
-- For multiple GPUs:
+- Training using multiple GPUs:
 ```
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python -m paddle.distributed.launch \
@@ -106,17 +127,74 @@ python -m paddle.distributed.launch \
 ```
 
 
-**Online Tutorial running in AI Studio:**
-
-* **Multi-faces swapping: https://aistudio.baidu.com/aistudio/projectdetail/1603391**
-* **Single face swapping: https://aistudio.baidu.com/aistudio/projectdetail/1586056**
 
 ## Animation results
 
-![](../../imgs/first_order.gif)
+  [![img](https://user-images.githubusercontent.com/48054808/119469551-0a377b00-bd7a-11eb-9117-e4871c8fb9c0.gif)](https://user-images.githubusercontent.com/48054808/119469551-0a377b00-bd7a-11eb-9117-e4871c8fb9c0.gif)
+
+
+
+### 3. Model Compression
+
+**Prediction:**
+
+```
+cd applications/
+python -u tools/first-order-demo.py  \
+     --driving_video ../docs/imgs/mayiyahei.MP4 \
+     --source_image ../docs/imgs/father_23.jpg \
+     --config ../configs/firstorder_vox_mobile_256.yaml \
+     --ratio 0.4 \
+     --relative \
+     --adapt_scale \
+     --mobile_net
+```
+
+Currently, we use mobilenet combined with pruning to compress models, see the comparison below:
+
+|            | Size(M) | reconstruction loss |
+| ---------- | ------- | ------------------- |
+| Original   | 229     | 0.012058867         |
+| Compressed | 6.1     | 0.015025159         |
+
+**Training:** First, set mode in configs/firstorder_vox_mobile_256.yaml as kp_detector, train the compressed kp_detector model, and immobilize the original  generator model. Then set mode in configs/firstorder_vox_mobile_256.yaml as generator，train the compressed generator model, and immobilize the original kp_detector model. Finally, set mode as both and modify kp_weight_path and gen_weight_path in the config to the path of trained model to train together。
+
+```
+export CUDA_VISIBLE_DEVICES=0
+python tools/main.py --config-file configs/firstorder_vox_mobile_256.yaml
+```
+
+
+
+### 4. Deployment
+
+#### 4.1 Export
+
+Use the `tools/fom_export.py` script to export the configuration file used when the model has been deployed with the config name of `firstorder_vox_mobile_256.yml`. The export script of the model is as follows.
+
+```
+# Export FOM Model
+
+python tools/export_model.py \
+    --config-file configs/firstorder_vox_mobile_256.yaml \
+    --load /root/.cache/ppgan/vox_mobile.pdparams \
+    --inputs_size "1,3,256,256;1,3,256,256;1,10,2;1,10,2,2" \
+    --export_model output_inference/
+```
+
+The prediction models will be exported to the directory of `output_inference/fom_dy2st/` as `model.pdiparams`,  `model.pdiparams.info`, `model.pdmodel`。
+
+- [Pre-training Model](https://paddlegan.bj.bcebos.com/applications/first_order_model/paddle_lite/inference/lite.zip)
+
+#### 4.2 Deployment of PaddleLite
+
+- [Deployment of FOM model  with Paddle Lite](https://github.com/PaddlePaddle/PaddleGAN/tree/develop/deploy/lite)
+
+- [FOM-Lite-Demo](https://paddlegan.bj.bcebos.com/applications/first_order_model/paddle_lite/apk/face_detection_demo 2.zip)。For more details, please refer to [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) .
 
+  Current problems： (a).Paddle Lite performs slightly worse than Paddle Inference，under optimization (b).Run Generator in a single thread, if the number of frames is too large, it will run at the small core rather than the large core.
 
-## Reference
+## References
 
 ```
 @InProceedings{Siarohin_2019_NeurIPS,