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Adaptive semantic attribute decoupling for precise face image editing
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DOI：10.1007/s00371-021-02198-z.png)
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Abstract
En 中文
Precisely editing user specified facial attributes has wide applications in areas such as virtual makeup, face aging, facial expression transfer, face synthesis. However, it is difficult to explicitly control individual facial attribute due to the gap between high level semantics in human perception and feature vectors in latent space. In this paper, a semantic disentanglement algorithm interpreting the latent space of GAN is proposed, which can be employed to extract attribute control vector adaptive to individual face. By adjusting the coefficient of extracted control vector, variation of single attribute is realized. Then, comprehensive modification effect of facial attributes is obtained through the superposition of control vector. Classification and content loss functions are introduced to limit modification occurs to the specified attribute without affecting the other attributes. As a result, precise editing control is realized.
Keywords:
Face attribute editing
GAN
Quantitative control
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