1. Install the dependencies in env.yml

   conda env create -f env.yml

   conda activate latent3d-env

2. Install pytorch3d by running the following:

   import sys
   import torch
   import subprocess
   version_str="".join([
       f"py3{sys.version_info.minor}_cu",
       torch.version.cuda.replace(".",""),
       f"_pyt{torch.__version__.split("+")[0].replace(".", "")}"
   ])
   subprocess.run(f"conda install https://anaconda.org/pytorch3d/pytorch3d/0.6.2/download/linux-64/pytorch3d-0.6.2-{version_str}.tar.bz2", shell=True)

3. Download the pre-trained TBGAN model from this link: https://ibug.doc.ic.ac.uk/resources/tbgan/.

• Note: You will be required to fill the form for 'End User Licence Agreement'.

4. Place the pre-trained TBGAN model snapshot in the '/models/snapshots' directory.

5. Download the pre-trained ArcFace model

   wget https://www.dropbox.com/s/kzo52d9neybjxsb/model_ir_se50.pth?dl=0 -O model_ir_se50.pth

6. Place the pre-trained ArcFace model in the '/models/snapshots' directory.

Running the 'optimize.py' with the required parameters and list of text prompts for manipulation, the rendered manipulated 3D faces and their originals will be saved under the direcory './results'.

python3 optimize.py --seed [SEED] --mode [MODE] --text_list [TEXT_LIST] --num_epochs [NUM_EPOCHS] --lambda_id [ID_COEFF] --lambda_l2 [L2_COEFF] --learning_rate [LEARNING_RATE]

• Example: python3 optimize.py --seed 1000 --mode text-based --text_list "happy human" "happy person" --num_epochs 100 --lambda_id 0.01 --lambda_l2 0.0 --learning_rate 0.01 --folder_title happy

Running the 'optimize.py' with given parameters and an image for manipulation, the rendered manipulated 3D faces and their originals will be saved under the direcory './results'.

python3 optimize.py --seed [SEED] --mode [MODE] --image_path [IMAGE_PATH] --num_epochs [NUM_EPOCHS] --lambda_id [ID_COEFF] --lambda_l2 [L2_COEFF] --learning_rate [LEARNING_RATE]

python3 sample.py --functionality latent_vector --result_subdir ./results/003-optimize