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Ray Conditioning: Trading Photo-realism for Photo-consistency in Multi-view Image Generation

Official PyTorch implementation of the ICCV 2023 paper</sub>

Ray Conditioning: Trading Photo-realism for Photo-consistency in Multi-view Image Generation<br> Eric Ming Chen, Sidhanth Holalkere, Ruyu Yan, Kai Zhang, Abe Davis<br> https://ray-cond.github.io<br>

Abstract: Multi-view image generation attracts particular attention these days due to its promising 3D-related applications, e.g., image viewpoint editing. Most existing methods follow a paradigm where a 3D representation is first synthesized, and then rendered into 2D images to ensure photo-consistency across viewpoints. However, such explicit bias for photo-consistency sacrifices photo-realism, causing geometry artifacts and loss of fine-scale details when these methods are applied to edit real images. To address this issue, we propose ray conditioning, a geometry-free alternative that relaxes the photo-consistency constraint. Our method generates multi-view images by conditioning a 2D GAN on a light field prior. With explicit viewpoint control, state-of-the-art photo-realism and identity consistency, our method is particularly suited for the viewpoint editing task.

Using networks from Python

This repo is built on top of stylegan3, and uses the camera conventions of eg3d.

You can use pre-trained networks in your own Python code as follows:

with open('ffhq-raycond2-512x512.pkl', 'rb') as f:
    G = pickle.load(f)['G_ema'].cuda()  # torch.nn.Module
z = torch.randn([1, G.z_dim]).cuda()    # latent codes
c2w                                     # [1, 4, 4] sized Tensor
intrinsics                              #[1, 3, 3] sized Tensor
c = torch.cat([c2w.view(1, -1), intrinsics.view(1, -1)], dim=-1) # camera parameters
img = G(z, c)                           # NCHW, float32, dynamic range [-1, +1], no truncation

We also provide visualization notebooks. There is one for each dataset.

• FFHQ
• AFHQ
• ShapeNet Cars

Pretrained Networks

You can download pretrained networks from here: RayConditioningCheckpoints.zip, and put them in the checkpoints folder.

Preparing datasets

Datasets are prepared with the dataset_preprocessing scripts from EG3D. The dataset requires camera poses and intrinsics for every image.

Training

The training script lies in train.py. The training parameters are exactly the same as those of StyleGAN3. Examples of training scripts are stored in the slurm_scripts folder. Configurations are provided for both StyleGAN2 and StyleGAN3, and are labeled as:

• raycond2
• raycond3-t
• raycond3-r

Here is an example training command for FFHQ:

python train.py --outdir=training-runs --data=/path/to/eg3d-ffhq.zip --cfg=raycond2 --gpus=2 --batch=32 --gamma=1 --snap=20 --cond=1 --aug=noaug --resume=checkpoints/stylegan2-ffhq-512x512.pkl

Citation

@InProceedings{chen2023:ray-conditioning,
    author    = {Chen, Eric Ming and Holalkere, Sidhanth and Yan, Ruyu and Zhang, Kai and Davis, Abe},
    title     = {Ray Conditioning: Trading Photo-consistency for Photo-realism in Multi-view Image Generation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {23242-23251}
}