Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN. (arXiv:2204.12696v1 [cs.CV])

The disentanglement of StyleGAN latent space has paved the way for realistic
and controllable image editing, but does StyleGAN know anything about temporal
motion, as it was only trained on static images? To study the motion features
in the latent space of StyleGAN, in this paper, we hypothesize and demonstrate
that a series of meaningful, natural, and versatile small, local movements
(referred to as "micromotion", such as expression, head movement, and aging
effect) can be represented in low-rank spaces extracted …

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