A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
Xingang Pan, Xudong Xu, Chen Change Loy, Christian Theobalt, Bo Dai
NeurIPS2021

In this repository, we present ShadeGAN, a generative model for shape-accurate 3D-aware image synthesis.
Our method adopts a multi-lighting constraint that resolves the shape-color ambiguity and leads to more accurate 3D shapes.

• python>=3.7
• pytorch>=1.8.1
• other dependencies

  pip install -r requirements.txt

To download lighting priors and pretrained weights, simply run:

sh scripts/download.sh

sh scripts/render.sh

This would generate images of multiple viewpoints and lightings by default.

python extract_shapes.py weights/pretrain/celeba_noview/generator.pth --curriculum CelebA_ShadeGAN_noview --seed 0 5 8 43 --ema

To evaluate metrics, you need to download dataset first as mentioned in Training below.

To generate real images for evaluation run

python fid_evaluation.py --dataset CelebA --dataset_path path/to/dataset/\*.jpg

To calculate fid/kid/inception scores run

python eval_metrics.py weights/pretrain/celeba_view/generator.pth --real_image_dir EvalImages/CelebA_real_images_128 --curriculum CelebA_ShadeGAN_view --num_steps 6 --delta 0.06423 --ema

where delta denotes the integration range along the ray for volume rendering. We record the delta for different pretrained models at weights/pretrain/delta.txt.

CelebA: Download at CelebA website
Cats: Please follow the instruction at GRAF
BFM: Please follow the instruction at Unsup3d

Before training, please update the dataset_path field in the curriculum to point to your images.

We provide our training scripts under scripts folder. For example, to train ShadeGAN on the CelebA dataset, simply run:

sh scripts/run_celeba.sh

This would run on 4 GPUs by default. You may change the number of GPUs by revising CUDA_VISIBLE_DEVICES in the scripts.

• If the number of GPUs for training is changed, you may need to adjust the batch_size in the curriculum to keep the total batchsize the same.
• In case of 'out of memory', you could increase batch_split in the curriculum to reduce memory consumption.
• For CelebA and BFM, both models depedent and independent of viewing direction are provided. The former has better FID while the latter has slightly better shapes.
• For BFM dataset, training could sometimes fall to the hollow-face solution where the face is concave. To prevent this, you could initialize with our pretrained models such as weights/pretrain/bfm_noview/pretrain5k-*.

This code is developed based on the official pi-GAN implementation.

If you find our work useful in your research, please cite:

@inproceedings{pan2021shadegan,
    title   = {A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis},
    author  = {Pan, Xingang and Xu, Xudong and Loy, Chen Change and Theobalt, Christian and Dai, Bo},
    booktitle = {Advances in Neural Information Processing Systems},
    year    = {2021}
}