  Human motion stylization aims to revise the style of an input motion while
keeping its content unaltered. Unlike existing works that operate directly in
pose space, we leverage the latent space of pretrained autoencoders as a more
expressive and robust representation for motion extraction and infusion.
Building upon this, we present a novel generative model that produces diverse
stylization results of a single motion (latent) code. During training, a motion
code is decomposed into two coding components: a deterministic content code,
and a probabilistic style code adhering to a prior distribution; then a
generator massages the random combination of content and style codes to
reconstruct the corresponding motion codes. Our approach is versatile, allowing
the learning of probabilistic style space from either style labeled or
unlabeled motions, providing notable flexibility in stylization as well. In
inference, users can opt to stylize a motion using style cues from a reference
motion or a label. Even in the absence of explicit style input, our model
facilitates novel re-stylization by sampling from the unconditional style prior
distribution. Experimental results show that our proposed stylization models,
despite their lightweight design, outperform the state-of-the-art in style
reenactment, content preservation, and generalization across various
applications and settings. Project Page: https://murrol.github.io/GenMoStyle
