### Install Dependencies with Conda Source: https://github.com/renyurui/pirender/blob/main/README.md Use this snippet to create a new Conda environment and install PyTorch with CUDA support, along with other project dependencies. ```bash # 1. Create a conda virtual environment. conda create -n PIRenderer python=3.6 conda activate PIRenderer conda install -c pytorch pytorch=1.7.1 torchvision cudatoolkit=10.2 # 2. Install other dependencies pip install -r requirements.txt ``` -------------------------------- ### Download Demo Dataset Source: https://github.com/renyurui/pirender/blob/main/README.md Execute this script to download the demo dataset containing 514 test videos for inference. ```bash ./scripts/download_demo_dataset.sh ``` -------------------------------- ### Intuitive Control: Prepare DeepFaceRecon Environment Source: https://github.com/renyurui/pirender/blob/main/README.md Copy necessary scripts to the DeepFaceRecon_pytorch directory and navigate into it to prepare for intuitive control tasks. ```bash # 1. Copy the provided scrips to the folder `Deep3DFaceRecon_pytorch`. cp scripts/face_recon_videos.py ./Deep3DFaceRecon_pytorch cp scripts/extract_kp_videos.py ./Deep3DFaceRecon_pytorch cp scripts/coeff_detector.py ./Deep3DFaceRecon_pytorch cp scripts/inference_options.py ./Deep3DFaceRecon_pytorch/options cd Deep3DFaceRecon_pytorch ``` -------------------------------- ### Prepare VoxCeleb LMDB Dataset Source: https://github.com/renyurui/pirender/blob/main/README.md Run this Python script to convert video and 3DMM parameter data into an LMDB file for training and testing. ```bash python scripts/prepare_vox_lmdb.py \ --path path_to_videos \ --coeff_3dmm_path path_to_3dmm_coeff \ --out path_to_output_dir ``` -------------------------------- ### Train PIRenderer Model Source: https://github.com/renyurui/pirender/blob/main/README.md Initiate the training process for the PIRenderer model using the specified configuration file and number of GPUs. ```bash python -m torch.distributed.launch --nproc_per_node=4 --master_port 12345 train.py \ --config ./config/face.yaml \ --name face ``` -------------------------------- ### Download Trained Weights Source: https://github.com/renyurui/pirender/blob/main/README.md Use this script to download the pre-trained model weights for inference. ```bash ./scripts/download_weights.sh ``` -------------------------------- ### Intuitive Control: Control Source Image Source: https://github.com/renyurui/pirender/blob/main/README.md Control the source image using the PIRenderer model after preparing the DeepFaceRecon environment and extracting coefficients. ```bash # 3. control the source image with our model cd .. python -m torch.distributed.launch --nproc_per_node=1 --master_port 12345 intuitive_control.py \ --config ./config/face_demo.yaml \ --name face \ --no_resume \ --output_dir ./vox_result/face_intuitive \ --input_name ./demo_images ``` -------------------------------- ### Extracting 3DMM Coefficients for Videos Source: https://github.com/renyurui/pirender/blob/main/DatasetHelper.md Extracts 3DMM coefficients from videos using the previously extracted keypoints. Requires input video directory, keypoint directory, output directory, and model specific parameters like batch size, model name, and epoch. ```bash python face_recon_videos.py \ --input_dir path_to_videos \ --keypoint_dir path_to_keypoint \ --output_dir output_dir \ --inference_batch_size 100 \ --name=model_name \ --epoch=20 \ --model facerecon ``` -------------------------------- ### Copying Scripts for DeepFaceRecon Source: https://github.com/renyurui/pirender/blob/main/DatasetHelper.md Copies necessary scripts and options files to the DeepFaceRecon_pytorch directory. Ensure you are in the root of the DeepFaceRecon_pytorch directory after copying. ```bash cp scripts/face_recon_videos.py ./Deep3DFaceRecon_pytorch cp scripts/extract_kp_videos.py ./Deep3DFaceRecon_pytorch cp scripts/coeff_detector.py ./Deep3DFaceRecon_pytorch cp scripts/inference_options.py ./Deep3DFaceRecon_pytorch/options cd Deep3DFaceRecon_pytorch ``` -------------------------------- ### Intuitive Control: Extract 3DMM Coefficients Source: https://github.com/renyurui/pirender/blob/main/README.md Extract 3DMM coefficients from demo images using the coeff_detector script within the DeepFaceRecon_pytorch environment. ```bash # 2. Extracte the 3dmm coefficients of the demo images. python coeff_detector.py \ --input_dir ../demo_images \ --keypoint_dir ../demo_images \ --output_dir ../demo_images \ --name=model_name \ --epoch=20 \ --model facerecon ``` -------------------------------- ### Inference: Face Reenactment (Same Identity) Source: https://github.com/renyurui/pirender/blob/main/README.md Run face reenactment for the same identity using the provided configuration and output directory. ```bash # same identity python -m torch.distributed.launch --nproc_per_node=1 --master_port 12345 inference.py \ --config ./config/face_demo.yaml \ --name face \ --no_resume \ --output_dir ./vox_result/face_reenactment ``` -------------------------------- ### Extracting Facial Landmarks from Videos Source: https://github.com/renyurui/pirender/blob/main/DatasetHelper.md Extracts facial landmarks from input videos. Specify the input video directory, output directory for keypoints, and optionally device IDs and worker count for parallel processing. ```bash python extract_kp_videos.py \ --input_dir path_to_viodes \ --output_dir path_to_keypoint \ --device_ids 0,1,2,3 \ --workers 12 ``` -------------------------------- ### Inference: Face Reenactment (Cross Identity) Source: https://github.com/renyurui/pirender/blob/main/README.md Perform face reenactment across different identities by setting the --cross_id flag. ```bash # cross identity python -m torch.distributed.launch --nproc_per_node=1 --master_port 12345 inference.py \ --config ./config/face_demo.yaml \ --name face \ --no_resume \ --output_dir ./vox_result/face_reenactment_cross \ --cross_id ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.