Skip to content

Latest commit

 

History

History
44 lines (33 loc) · 2.51 KB

README.md

File metadata and controls

44 lines (33 loc) · 2.51 KB
Raw

US-GAN: On the importance of Ultimate Skip Connection for Facial Expression Synthesis (MTAP, 2023)

This repository provides the official implementation of the following paper:

US-GAN: On the importance of Ultimate Skip Connection for Facial Expression Synthesis
Arbish Akram and Nazar Khan
Department of Computer Science, University of the Punjab, Lahore, Pakistan.
In Multimedia Tools and Applications (MTAP), 2023

Abstract: We demonstrate the benefit of using an ultimate skip (US) connection for facial expression synthesis using generative adversarial networks (GAN). A direct connection transfers identity, facial, and color details from input to output while suppressing artifacts. The intermediate layers can therefore focus on expression generation only. This leads to a light-weight US-GAN model comprised of encoding layers, a single residual block, decoding layers, and an ultimate skip connection from input to output. US-GAN has 3x fewer parameters than state-of-the-art models and is trained on 2 orders of magnitude smaller dataset. It yields 7% increase in face verification score (FVS) and 27% decrease in average content distance (ACD). Based on a randomized user-study, US-GAN outperforms the state of the art by 25% in face realism, 43% in expression quality, and 58% in identity preservation.

Test with Pretrained Model

python driver.py --mode test --image_size 128 --c_dim 7 --image_dir ./testing_imgs/ --model_save_dir ./pre-trained_model/ \
                 --result_dir ./usgan/results

Train the Model

python driver.py --mode train --image_size 128 --c_dim 7 --batch_size 8 --image_dir ./dataset/  --model_save_dir ./usgan/models/ \
                 --log_dir ./usgan/logs --sample_dir ./usgan/samples --result_dir ./usgan/results

Citation

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

@article{akram23usgan,
  author={Akram, Arbish and Khan, Nazar},
  journal={Multimedia Tools and Applications}, 
  title={US-GAN: On the importance of Ultimate Skip Connection for Facial Expression Synthesis}, 
  year={2023},
  volume={},
  number={},
  pages={1-1},
  doi={10.1007/s11042-023-15268-2}}

Acknowledgement

This code is based on Yunjey's StarGAN with minimal modifications.