Representing motion as a sequence of latent primitives, a flexible approach for human motion modelling

TL;DR

This paper introduces a novel human motion representation using a sequence of latent primitives, improving long-term motion modeling and enabling flexible, continuous-time querying for various tasks.

Contribution

It extends existing motion priors by modeling long motions as sequences of primitives with a temporal segmentation, enhancing flexibility and accuracy.

Findings

Significant improvement in spatio-temporal completion tasks

Effective segmentation and representation of long human motions

Flexible, continuous-time motion querying capability

Abstract

We propose a new representation of human body motion which encodes a full motion in a sequence of latent motion primitives. Recently, task generic motion priors have been introduced and propose a coherent representation of human motion based on a single latent code, with encouraging results for many tasks. Extending these methods to longer motion with various duration and framerate is all but straightforward as one latent code proves inefficient to encode longer term variability. Our hypothesis is that long motions are better represented as a succession of actions than in a single block. By leveraging a sequence-to-sequence architecture, we propose a model that simultaneously learns a temporal segmentation of motion and a prior on the motion segments. To provide flexibility with temporal resolution and motion duration, our representation is continuous in time and can be queried for any timestamp. We show experimentally that our method leads to a significant improvement over state-of-the-art motion priors on a spatio-temporal completion task on sparse pointclouds. Code will be made available upon publication.