AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs
Key Notes
- A model capable of individually transforming the local aging signs. Aging process of the different parts of the face.
- Patch-based approach to enable inference on high-resolution images while keeping the computational cost of training the model low
Dataset - Flicker Faces High-Quality Dataset (FFHQ)
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PFA-GAN: Progressive Face Aging with Generative Adversarial Network
Key Notes
- Progressive face aging framework based on generative adversarial network (PFA-GAN) to model the face age progression in a progressive way
- We introduce an age estimation loss to take into account the age distribution for an improved aging accuracy
Triple-GAN: Progressive Face Aging with Triple Translation Loss
Key Notes
- GAN adopts triple translation loss to model the strong interrelationship of age patterns among different age groups
- By adopting triple translation loss, the progressive mappings of different age domains are fully correlated. The generator is encouraged to be reusable, generating synthesized faces with the more evident aging effect.
- Enhanced adversarial loss is adopted to effectively model the complex distribution of age domains
Dataset - Cross-Age Celebrity Dataset (CACD)
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