COMAND: Controllable Action-aware Manifold for 3D Motion Synthesis
No Thumbnail Available
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present COMAND, a novel method for controllable multi-action 3D motion synthesis without requiring action-labeled data. Our method can generate a lifelike motion sequence containing consecutive non-locomotive actions such as kicking, jumping, or squatting, without the need for manual blending, enabling an intuitive way to control 3D human animation based on the desired motion types at specified time windows. At the core of our method is a motion manifold based on a periodic parameterization of a motion latent space that allows for unsupervised action clustering of 3D motion, thus allowing action-to-motion synthesis without the need to explicitly train the model on action-labeled datasets. This learned motion manifold has semantic and periodic properties that benefit 3D motion synthesis from action labels and from free-form text input, resulting in a state-ofthe- art multi-modal and multi-action 3D motion generation framework. Our study shows that more than 83% and 96% of the users respectively rated COMAND as more natural and better matching the target action sequence when compared to existing methods.
Description
CCS Concepts: Computing methodologies → Motion capture
@inproceedings{10.2312:vmv.20241209,
booktitle = {Vision, Modeling, and Visualization},
editor = {Linsen, Lars and Thies, Justus},
title = {{COMAND: Controllable Action-aware Manifold for 3D Motion Synthesis}},
author = {Habibie, Ikhsanul and Elgharib, Mohamed and Luvizon, Diogo and Thambiraja, Balamurugan and Nyatsanga, Simbarashe and Thies, Justus and Neff, Michael and Theobalt, Christian},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-247-9},
DOI = {10.2312/vmv.20241209}
}