Environment

• GPU: 1 NVIDIA GeForce RTX 2080 Ti with 11GB memory is enough.
• OS: Linux Ubuntu 18.04 LTS
• IDE: Visual Studio Code 2022.09
• Others: Python3.7 + PyTorch1.8.1 + CUDA10.1

Preparation

• To prepare data and pretraind models, please check all the file folders in this project and follow the guidance in readme.md.
• To accelerate the training process, we precompute the 512-dim style code for every artistic human face, you can download style_codes.csv and place it under ArtNerf/.

Training

• The model is trained by conducting a two-stage training strategy: pretraining on CelebA and fine-tuning on both AAHQ and CelebA.
• The whole model is composed of 1 generator and 3 dicriminators. disc_real guides the gen to generate natural human faces and disc_style guides the gen to generate stylized human faces. disc_latent helps ensure the style-consistency between generated faces and ref faces.
• We use a style blending module to help stabilize the cross-domain transfer learning process and allow users to change the extent to which the generated images is stylized(level can be changed from 0 to 11).

Examples

Main results

Style Image	fake_a	fake_b (i = 0)	fake_b (i = 3)	fake_b (i = 11)

Here shows some other stylized face avatars with different resolutions.

64×64

128x128

Latent Space Interpolation

Following the traditional face synthesis models like StyleGAN, we can perform interpolation between any two latent codes.

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