Probabilistic Character Motion Synthesis using a Hierarchical Deep Latent Variable Model

TL;DR

This paper introduces a hierarchical deep latent variable model for probabilistic character motion synthesis that generates realistic, diverse animations from weak control signals, leveraging a variational autoencoder and novel evaluation protocols.

Contribution

It presents a hierarchical recurrent architecture with a specialized objective function for realistic, stochastic motion generation from weak controls, advancing animation synthesis methods.

Findings

Successfully generates convincing, diverse motion sequences

Outperforms existing methods in realism and variability

Validated through quantitative protocols and human assessments

Abstract

We present a probabilistic framework to generate character animations based on weak control signals, such that the synthesized motions are realistic while retaining the stochastic nature of human movement. The proposed architecture, which is designed as a hierarchical recurrent model, maps each sub-sequence of motions into a stochastic latent code using a variational autoencoder extended over the temporal domain. We also propose an objective function which respects the impact of each joint on the pose and compares the joint angles based on angular distance. We use two novel quantitative protocols and human qualitative assessment to demonstrate the ability of our model to generate convincing and diverse periodic and non-periodic motion sequences without the need for strong control signals.