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• Our code is adopted from EG3D and follow its requirements and data preparation.
• Create a environment

  conda env create -f environment.yml
  conda activate eg3d

• Follow EG3D to pre-process FFHQ, AFHQ, and ShapeNet data.
• Pretrained models are avaliable at Google Drive.
• The data and model folders look as follows:

  ROOT
      ├──data
          ├──AFHQ
              ├── adhq_v2_256.zip
              ├── adhq_v2_512.zip
          ├──FFHQ
              ├──FFHQ_256.zip
              ├──FFHQ_512.zip
          ├──ShapeNet
              |──car_128.zip
      ├──out
          ├──afhq256_2d
          ├──afhq256_3d
          ├──afhq512_2d
          ├──afhq512_3d
          ├──ffhq256_2d
          ├──ffhq256_3d
          ├──ffhq512_2d
          ├──ffhq512_3d
          ├──shapenet128_2d
          ├──shapenet128_3d
  

./scripts/infer.sh

• Results will be saved to out/{experiment}/infer

./scripts/val.sh

./scripts/train.sh

• All asserts produced by the training process will be saved to out/{experiment}

• In above .sh files, --cfg can be changed for different models.
• In a config file (e.g., configs/ffhq_3d.yaml), key settings are explained as follows:

  # your experiment name
  experiment: 'ffhq512_3d' 
  # it takes ~40G GPU memory if using 8 GPUs and a batch size of 32 to train a 512-size 3D model
  gpus: 8
  batch: 32
  # the resolution with 3D-aware conv
  aware3d_res: [4,8,16,32,64,128,256]
  # model to load; None for from-scratch; we suggest loading a 2D model before training a 3D model; also, a 3D model can be trained from scratch
  resume: '017000' 
  # loss weight for patch discrimination
  patch_gan: 0.1 
  # for 512-size model w/o 2D super-res., FID evaluation takes ~4h; you would want to set `metrics: []` to cancel evaluation durning training
  metrics: [] 
  # select from video|videos|image
  inference_mode: 'video' 
  # rendering resolution with radiance field in inference and evaluation
  neural_rendering_resolution_infer: 512 
  # which tri-plane used for rendering. 0: coarse & detail triplanes; 1: coarse triplane; 2: detail triplane
  coarse: 0 
  # return triplane or not
  retplane: -1 
  # extract geometry or not
  shapes: False 
  # reduce point amount in a forward pass to avoid OOM durning inference and evaluation
  chunk: 500000 

@inproceedings{bib:mimic3d,
  title={Mimic3D: Thriving 3D-Aware GANs via 3D-to-2D Imitation},
  author={Chen, Xingyu and Deng, Yu and Wang, Baoyuan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2023}
}

Our implementation is based on EG3D. We thank them for inspiring implementations.