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First Order Motion

First order motion model 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.

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

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

How to use

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:

Running Command：

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 \
     --image_size 512 \
     --face_enhancement \
     --multi_person

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 img

2. Training

Datasets:

  • fashion See here
  • VoxCeleb See here. 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

Parameters:

  • dataset_name.yaml: Configure your own yaml document and parameters

  • Training using single GPU:

export CUDA_VISIBLE_DEVICES=0
python tools/main.py --config-file configs/dataset_name.yaml
  • 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 \
    tools/main.py \
    --config-file configs/dataset_name.yaml

Example:

  • Training using single GPU:
export CUDA_VISIBLE_DEVICES=0
python tools/main.py --config-file configs/firstorder_fashion.yaml \
  • Training using multiple GPUs:
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch \
    tools/main.py \
    --config-file configs/firstorder_fashion.yaml \

Animation results

img

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 0.041781392
Compressed 10.1 0.047878753

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。

4.2 Deployment of PaddleLite

  • Deployment of FOM model with Paddle Lite

  • FOM-Lite-Demo。For more details, please refer to 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.

References

@InProceedings{Siarohin_2019_NeurIPS,
  author={Siarohin, Aliaksandr and Lathuilière, Stéphane and Tulyakov, Sergey and Ricci, Elisa and Sebe, Nicu},
  title={First Order Motion Model for Image Animation},
  booktitle = {Conference on Neural Information Processing Systems (NeurIPS)},
  month = {December},
  year = {2019}
}