Generative Human Motion Stylization in Latent Space

Understand

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.