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This code is the official PyTorch implementation of the paper:
Lifespan Age Synthesis with Geometry Guidance and Decorrelation Constraint
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(journal)
(arxiv link)
Please follow this github to prepare the environments.
Download the dataset from male and female and put them to ./datasets/males and ./datasets/females.
Training (please modify --dataroot, --name):
sh train_GGDC.sh
Testing (please modify --dataroot, --name, --which_epoch, and --checkpoing_dir):
sh test_GGDC.sh
You can run the demo locally or explore it in Colab
If you wish to train the model on a new dataset, arrange it in the following structure:
├── dataset_name
│ ├── train<class1>
| | └── image1.png
| | └── image2.png
| | └── ...
│ │ ├── parsings
│ │ │ └── image1.png
│ │ │ └── image2.png
│ │ │ └── ...
│ │ ├── landmarks
│ │ │ └── image1.png
│ │ │ └── image2.png
│ │ │ └── ...
...
│ ├── train<classN>
| | └── image1.png
| | └── image2.png
| | └── ...
│ │ ├── parsings
│ │ │ └── image1.png
│ │ │ └── image2.png
│ │ │ └── ...
│ │ ├── landmarks
│ │ │ └── image1.png
│ │ │ └── image2.png
│ │ │ └── ...
│ ├── test<class1>
| | └── image1.png
| | └── image2.png
| | └── ...
│ │ ├── parsings
│ │ │ └── image1.png
│ │ │ └── image2.png
│ │ │ └── ...
...
│ ├── test<classN>
| | └── image1.png
| | └── image2.png
| | └── ...
│ │ ├── parsings
│ │ │ └── image1.png
│ │ │ └── image2.png
│ │ │ └── ...
If you use this code for your research, please cite our paper.