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Look globally, age locally: Face aging with an attention mechanism (AcGANs)

PyTorch implementation of the AcGANs algorithm in the paper ``Look globally, age locally: Face aging with an attention mechanism.''.

1. The Architecture of AcGANs

Architecture of AcGAN

2. Prerequisites

  • Python 3.6

  • PyTorch 1.3.0

  • GPU

3. Dataset & Preparation

4. Training

Training a model by:

$ python main.py config/morph.yml

5. Results

  • Attention Results

  • attention_results

  • Results on the Morph Dataset

  • results_on_morph

  • Comparison of AcGANs, IPCGANs, and CAAE in the Morph Dataset

    comparison_result

6. Citation

Zhu H, Huang Z, Shan H, et al. Look globally, age locally: Face aging with an attention mechanism[J]. arXiv preprint arXiv:1910.12771, 2019.

7. License

AcGANs is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact Junping Zhang.