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The disentanglement of StyleGAN latent space has paved the way for realisticand controllable image editing, but does StyleGAN know anything about temporalmotion, as it was only trained on static images? To study the motion featuresin the latent space of StyleGAN, in this paper, we hypothesize and demonstratethat a series of meaningful, natural, and versatile small, local movements(referred to as "micromotion", such as expression, head movement, and agingeffect) can be represented in low-rank spaces extracted from the latent spaceof a conventionally pre-trained StyleGAN-v2 model for face generation, with theguidance of proper "anchors" in the form of either short text or video clips.Starting from one target face image, with the editing direction decoded fromthe low-rank space, its micromotion features can be represented as simple as anaffine transformation over its latent feature. Perhaps more surprisingly, suchmicromotion subspace, even learned from just single target face, can bepainlessly transferred to other unseen face images, even those from vastlydifferent domains (such as oil painting, cartoon, and sculpture faces). Itdemonstrates that the local feature geometry corresponding to one type ofmicromotion is aligned across different face subjects, and hence thatStyleGAN-v2 is indeed "secretly" aware of the subject-disentangled featurevariations caused by that micromotion. We present various successful examplesof applying our low-dimensional micromotion subspace technique to directly andeffortlessly manipulate faces, showing high robustness, low computationaloverhead, and impressive domain transferability. Our codes are available athttps://github.com/wuqiuche/micromotion-StyleGAN.

Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN