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InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing

Python 3.7 pytorch 1.1.0 TensorFlow 1.12.2 sklearn 0.21.2

image Figure: High-quality facial attributes editing results with InterFaceGAN.

In this repository, we propose an approach, termed as InterFaceGAN, for semantic face editing. Specifically, InterFaceGAN is capable of turning an unconditionally trained face synthesis model to controllable GAN by interpreting the very first latent space and finding the hidden semantic subspaces.

[Paper (CVPR)] [Paper (TPAMI)] [Project Page] [Demo] [Colab]

How to Use

Pick up a model, pick up a boundary, pick up a latent code, and then EDIT!

# Before running the following code, please first download
# the pre-trained ProgressiveGAN model on CelebA-HQ dataset,
# and then place it under the folder ".models/pretrain/".
LATENT_CODE_NUM=10
python edit.py \
    -m pggan_celebahq \
    -b boundaries/pggan_celebahq_smile_boundary.npy \
    -n "$LATENT_CODE_NUM" \
    -o results/pggan_celebahq_smile_editing

GAN Models Used (Prior Work)

Before going into details, we would like to first introduce the two state-of-the-art GAN models used in this work, which are ProgressiveGAN (Karras el al., ICLR 2018) and StyleGAN (Karras et al., CVPR 2019). These two models achieve high-quality face synthesis by learning unconditional GANs. For more details about these two models, please refer to the original papers, as well as the official implementations.

ProgressiveGAN: [Paper] [Code]

StyleGAN: [Paper] [Code]

Code Instruction

Generative Models

A GAN-based generative model basically maps the latent codes (commonly sampled from high-dimensional latent space, such as standart normal distribution) to photo-realistic images. Accordingly, a base class for generator, called BaseGenerator, is defined in models/base_generator.py. Basically, it should contains following member functions:

We have already provided following models in this repository:

Utility Functions

We provide following utility functions in utils/manipulator.py to make InterFaceGAN much easier to use.

Tools

Usage

We take ProgressiveGAN model trained on CelebA-HQ dataset as an instance.

Prepare data

NUM=10000
python generate_data.py -m pggan_celebahq -o data/pggan_celebahq -n "$NUM"

Predict Attribute Score

Get your own predictor for attribute $ATTRIBUTE_NAME, evaluate on all generated images, and save the inference results as data/pggan_celebahq/"$ATTRIBUTE_NAME"_scores.npy. NOTE: The save results should be with shape ($NUM, 1).

Search Semantic Boundary

python train_boundary.py \
    -o boundaries/pggan_celebahq_"$ATTRIBUTE_NAME" \
    -c data/pggan_celebahq/z.npy \
    -s data/pggan_celebahq/"$ATTRIBUTE_NAME"_scores.npy

Compute Conditional Boundary (Optional)

This step is optional. It depends on whether conditional manipulation is needed. Users can use function project_boundary() in file utils/manipulator.py to compute the projected direction.

Boundaries Description

We provided following boundaries in folder boundaries/. The boundaries can be more accurate if stronger attribute predictor is used.

BibTeX

@inproceedings{shen2020interpreting,
  title     = {Interpreting the Latent Space of GANs for Semantic Face Editing},
  author    = {Shen, Yujun and Gu, Jinjin and Tang, Xiaoou and Zhou, Bolei},
  booktitle = {CVPR},
  year      = {2020}
}
@article{shen2020interfacegan,
  title   = {InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs},
  author  = {Shen, Yujun and Yang, Ceyuan and Tang, Xiaoou and Zhou, Bolei},
  journal = {TPAMI},
  year    = {2020}
}