Probabilistic Character Motion Synthesis using a Hierarchical Deep Latent Variable Model

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‐...

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Bibliographic Details
Published in:Computer graphics forum Vol. 39; no. 8; pp. 225 - 239
Main Authors: Ghorbani, S., Wloka, C., Etemad, A., Brubaker, M. A., Troje, N. F.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.12.2020
Subjects:
CCS Concepts
Computing methodologies → Animation
Human motion
Machine learning approaches
Probability theory
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary: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.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14116