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pix2pix demo that learns from facial landmarks and translates this into a face
| Date | Stars |
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| 2026-07-24 | 1461 |
| 2026-07-25 | 1461 |
| 2026-07-28 | 1461 |
| 2026-07-30 | 1461 |
| 2026-08-06 | 1461 |
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# face2face-demo This is a pix2pix demo that learns from facial landmarks and translates this into a face. A webcam-enabled application is also provided that translates your face to the trained face in real-time. ## Getting Started #### 1. Prepare Environment ``` # Clone this repo git clone [email protected]:datitran/face2face-demo.git # Create the conda environment from file (Mac OSX) conda env create -f environment.yml ``` #### 2. Generate Training Data ``` python generate_train_data.py --file angela_merkel_speech.mp4 --num 400 --landmark-model shape_predictor_68_face_landmarks.dat ``` Input: - `file` is the name of the video file from which you want to create the data set. - `num` is the number of train data to be created. - `landmark-model` is the facial landmark model that is used to detect the landmarks. A pre-trained facial landmark model is provided [here](http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2). Output: - Two folders `original` and `landmarks` will be created. If you want to download my dataset, here is also the [video file](https://dl.dropboxusercontent.com/s/2g04onlkmkq9c69/angela_merkel_speech.mp4) that I used and the generated [training dataset](https://dl.dropboxusercontent.com/s/pfm8b0yogmum63w/dataset.zip) (400 images already split into training and validation). #### 3. Train Model ``` # Clone the repo from Christopher Hesse's pix2pix TensorFlow implementation git clone https://github.com/affinelayer/pix2pix-tensorflow.git # Move the original and landmarks folder into the pix2pix-tensorflow folder mv face2face-demo/landmarks face2face-demo/original pix2pix-tensorflow/photos # Go into the pix2pix-tensorflow folder cd pix2pix-tensorflow/ # Resize original images python tools/process.py \ --input_dir photos/original \ --operation resize \ --output_dir photos/original_resized # Resize landmark images python tools/process.py \ --input_dir photos/landmarks \ --operation resize \ --output_dir photos/landmarks_resized # Combine both resized original and landmark images python tools/process.py \ --input_dir photos/landmarks_resized \ --b_dir photos/original_resized \ --operation combine \ --output_dir photos/combined # Split into train/val set python tools/split.py \ --dir photos/combined # Train the model on the data python pix2pix.py \ --mode train \ --output_dir face2face-model \ --max_epochs 200 \ --input_dir photos/combined/train \ --which_direction AtoB ``` For more information around training, have a look at Christopher Hesse's [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow) implementation. #### 4. Export Model 1. First, we need to reduce the trained model so that we can use an image tensor as input: ``` python reduce_model.py --model-input face2face-model --model-output face2face-reduced-model ``` Input: - `model-input` is the model folder to be imported. - `model-output` is the model (reduced) folder to be exported. Output: - It returns a reduced model with less weights file size than the original model. 2. Second, we freeze the reduced model to a single file. ``` python freeze_model.py --model-folder face2face-reduced-model ``` Input: - `model-folder` is the model folder of the reduced model. Output: - It returns a frozen model file `frozen_model.pb` in the model folder. I have uploaded a pre-trained frozen model [here](https://dl.dropboxusercontent.com/s/rzfaoeb3e2ta343/face2face_model_epoch_200.zip). This model is trained on 400 images with epoch 200. #### 5. Run Demo ``` python run_webcam.py --source 0 --show 0 --landmark-model shape_predictor_68_face_landmarks.dat --tf-model face2face-reduced-model/frozen_model.pb ``` Input: - `source` is the device index of the camera (default=0). - `show` is an option to either display the normal input (0) or the facial landmark (1) alongside the generat
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Dat Tran · DATANOMIQ · Germany
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6a0f03f0c4c4fa67, topic:tensorflow