Tensor-based Emotion Editing in the StyleGAN Latent Space

Publikation: Konferencebidrag - EJ publiceret i proceeding eller tidsskriftPaperForskningpeer review


In this paper, we use a tensor model based on the Higher-Order Singular Value Decomposition (HOSVD) to discover semantic directions in Generative Adversarial Networks. This is achieved by first embedding a structured facial expression database into the latent space using the e4e encoder. Specifically, we discover directions in latent space corresponding to the six prototypical emotions: anger, disgust, fear, happiness, sadness, and surprise, as well as a direction for yaw rotation. These latent space directions are employed to change the expression or yaw rotation of real face images. We compare our found directions to similar directions found by two other methods. The results show that the visual quality of the resultant edits are on par with State-of-the-Art. It can also be concluded that the tensor-based model is well suited for emotion and yaw editing, i.e., that the emotion or yaw rotation of a novel face image can be robustly changed without a significant effect on identity or other attributes in the images.
Publikationsdato13 maj 2022
Antal sider10
StatusUdgivet - 13 maj 2022
BegivenhedAI for Content Creation Workshop
@ CVPR 2022
- New Orleans Ernest N. Morial Convention Center, New Orleans, USA
Varighed: 19 jun. 202220 jun. 2022


KonferenceAI for Content Creation Workshop
@ CVPR 2022
LokationNew Orleans Ernest N. Morial Convention Center
ByNew Orleans


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