Researchers from Korea University, Clova AI Research (NAVER), The College of New Jersey, and Hong Kong University of Science & Technology developed a Generative Adversarial Networks (GAN)-based approach that transforms the facial expressions of still images.
Using an NVIDIA Tesla GPU and the cuDNN-accelerated PyTorch deep learning framework, the team trained their models on the CelebFaces Attributes (CelebA) dataset and the Radboud Faces Database (RaFD) that includes of a variety facial expressions. Their framework named StarGAN is able to perform multi-domain image-to-image translation results on the CelebA dataset via transferring the knowledge it learned from the RaFD dataset – this means they can take an input image of a neutral celebrity face and synthesize the facial expressions (angry, happy, and fearful).
The researchers claim this work is the first to successfully perform multi-domain image translation across different datasets.
The framework is also able to transfer facial attributes to an input image to automatically age someone, or change the color of their hair.
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Nov 29, 2017
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AI-Generated Summary
- Researchers from several universities and institutions developed a Generative Adversarial Networks (GAN)-based approach to change facial expressions in still images using NVIDIA Tesla GPU and cuDNN-accelerated PyTorch.
- The team's framework, named StarGAN, was trained on the CelebFaces Attributes (CelebA) dataset and the Radboud Faces Database (RaFD) to perform multi-domain image-to-image translation, allowing it to synthesize various facial expressions.
- StarGAN can also transfer facial attributes to an input image, such as aging someone or changing their hair color.
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