README.md

GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes

Official implementation of the paper GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes.

Appeared in BMVC 2021 and CVPR's AI for content creation workshop.

Environment

CUDA 10
tensorflow-gpu==1.12.0
tensorpack==0.9.0
scikit-video ==1.1.11
python-opencv
dominate

Datasets

Download datasets from the link. Unzip datasets.zip in the project root (gaussigan/datasets).

Training

Mask training

To train a shape model on a dynamic object from scratch. Run the command bellow:

cd mask
./train_giraffe.sh

To train on a static object use ./mask_gen_static.py.

Texture training

You train a texture model on a dynamic object from scratch. First download a pretrained vgg model from here. Unzip vgg.zip in gaussigan/rgb

Start training via the command below:

cd rgb
./train_giraffe.sh

To train on a static object use ./rgb_gen_static.py.

To load a model, add the --load argument followed by the path to the folder containing the corresponding checkpoints.

Inference

You can run mask inference via (--modelpath is the folder containing the frozen models):

cd mask
python infer_masks.py --modelpath ./train_log/gitaffe/.../20210322-214818  --ims ../datasets/rgbs/giraffe --masks ../datasets/masks/giraffe

You can run texture inference via:

cd rgb
python infer_rgbs.py --modelpath ./train_log/gitaffe/.../20210322-214818 --ims ../datasets/rgbs/giraffe --masks ../datasets/masks/giraffe

Pretrained models:

Download pretrained models from the link. Unzip pretrained.zip in the project root, and use the models in infence mode to get results. They will be stored alongside the checkpoints.