Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

Baris Gecer ¹, Binod Bhattarai ¹, Josef Kittler ², & Tae-Kyun Kim ^1
1 Department of Electrical and Electronic Engineering, Imperial College London, UK
² Centre for Vision, Speech and Signal Processing, University of Surrey, UK

This repository provides a Tensorflow implementation of our study where we propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model.

Approach

Dependencies

• TensorFlow 1.2
• Python 3.5

Data

• Generate synthetic images using any 3DMM model i.e. LSFM or Basel Face Model by running gen_syn_latent.m
• Align and crop all datasets using MTCNN to 108x108

Usage

Train by the following script

$ python main.py    --log_dir [path2_logdir] --data_dir [path2_datadir] --syn_dataset [synthetic_dataset_name]
                    --dataset [real_dataset_name] --dataset_3dmm [300W-3D & AFLW2000_dirname] --input_scale_size 108

Add --load_path [paused_training_logdir] to continue a training

Generate realistic images after training by the following script

$ python main.py    --log_dir [path2_logdir] --data_dir [path2_datadir] --syn_dataset [synthetic_dataset_name]
                    --dataset [real_dataset_name] --dataset_3dmm [300W-3D & AFLW2000_dirname] --input_scale_size 108
                    --save_syn_dataset [saving_dir] --train_generator False --generate_dataset True --pretrained_gen [path2_logdir + /model.ckpt]

Pretrained Model

You can download the pretrained model

More Results

Citation

if you find this work is useful for your research, please cite our paper:

@inproceedings{gecer2018semi,
  title={Semi-supervised adversarial learning to generate photorealistic face images of new identities from 3D morphable model},
  author={Gecer, Baris and Bhattarai, Binod and Kittler, Josef and Kim, Tae-Kyun},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={217--234},
  year={2018}
}

Acknowledgement

This work was supported by the EPSRC Programme Grant ‘FACER2VM’ (EP/N007743/1). Baris Gecer is funded by the Turkish Ministry of National Education. This study is morally motivated to improve face recognition to help prediction of genetic disorders visible on human face in earlier stages.

Code borrows heavily from carpedm20's BEGAN implementation.