TT-GNeRF

Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Jichao Zhang, Aliaksandr Siarohin, Yahui Liu, Hao Tang, Nicu Sebe, Wei Wang
paper Demo-Video
University of Trento, Snap Research, ETH Zurich

Environments

conda create -n ttgnerf python=3.6

pip install -r req.txt

Results

Editing (EG3D)

Please edit the file training/loss.py to change the path of BiSeNet model. You can download BiSeBet from the given pretrained model path.

python train_step_Triot_styleflow.py --outdir=[output_path] \
            --network=[pretrained eg3d model] \
            --dataset_path [our dataset path] \
            --csvpath [label path] \
            --cnf_path=[cnf pretrained model path] \
            --batch=1 \
            --gen_pose_cond=True \
            --resolution 512 \
            --label_dim 6 \
            --truncation_psi 0.7 \
            --scale 1.5 \
            --finetune_id 2 \
            --file_id 202 \
            --num_steps 100 \
            --lambda_normal 1.0

Inversion (EG3D)

PTI method

python inversionv2.py --outdir=[output_path_v1] --trunc=0.7 --shapes=true --seeds=0-3 \
    --network=[pretrained eg3d model] --reload_modules True --file_id 20 &

wait

python3 inversion_PTI.py --outdir=[output_path_v2] \
    --trunc=0.7 --shapes=true --seeds=0-3 \
    --network=[output_path_v1] --reload_modules True --file_id 20

TRIOT

python train_step_Triot_PTI_styleflow.py --outdir=[output_path_v3] \
            --latent_dir [output_path_v1] \
            --network=[output_path_v2] \
            --cnf_path=[cnf pretrained model path] \
            --dataset_path=[our dataset path] \
            --csvpath [label path] \
            --batch=1 \
            --gen_pose_cond=True \
            --resolution 512 \
            --gen_pose_cond True \
            --label_dim 6 \
            --truncation_psi 0.7 \
            --scale 1.2 \
            --finetune_id 3 \
            --file_id 17 \
            --num_steps 150 \

Reference Image Geometry Transfer

python train_step_reference_geometry_editing.py --outdir=[output_path] --batch=1 \
            --gen_pose_cond=True --num_steps 400 \
            --w_dir [our dataset path] \
            --resolution 512 --truncation_psi 0.7 --id 13 --ref_id 41

Pretrained Model and Dataset

our dataset path:

cnf pretrained model path:

label path:

pretrained eg3d model:

Questions

If you have any questions/comments, feel free to open a github issue or pull a request or e-mail to the author Jichao Zhang (jichao.zhang@unitn.it).

Reference code

We would like to thank EG3D and StyleFlow for providing such a great and powerful codebase.