MVAE : Motion-conditioned Variational Auto-Encoder for tailoring character animations
Loading...
Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The design of character animations with enough diversity is a time-consuming task in many productions such as video games or animated films, and drives the need for more simple and effective authoring systems. This paper introduces a novel approach, a motion-conditioned variational autoencoder (VAE) with Virtual reality as a motion capture device. Our model generates diverse humanoid character animations only based on a gesture captured from two Virtual reality controllers, allowing for precise control of motion characteristics such as rhythm, speed and amplitude, and providing variability through noise sampling. From a dataset comprising paired controller-character motions, we design and train our VAE to (i) identify global motion characteristics from the movement, in order to discern the type of animation desired by the user, and (ii) identify local motion characteristics including rhythm, velocity, and amplitude to adapt the animation to these characteristics. Unlike many text-tomotion approaches, our method faces the challenge of interpreting high-dimensional, non-discrete user inputs. Our model maps these inputs into the higher-dimensional space of character animation while leveraging motion characteristics (such as height, speed, walking step frequency, and amplitude) to fine-tune the generated motion. We demonstrate the relevance of the approach on a number of examples and illustrate how changes in rhythm and amplitude of the input motions are transferred to coherent changes in the animated character, while offering a diversity of results using different noise samples.
Description
CCS Concepts: Computing methodologies → Neural networks; Animation; Machine learning; Deep learning; Motion processing
@inproceedings{10.2312:exw.20251059,
booktitle = {ACM/EG Expressive Symposium - WICED: Eurographics Workshop on Intelligent Cinematography and Editing},
editor = {Catalano, Chiara Eva and Parakkat, Amal Dev},
title = {{MVAE : Motion-conditioned Variational Auto-Encoder for tailoring character animations}},
author = {Bordier, Jean-Baptiste and Christie, Marc},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-271-4},
DOI = {10.2312/exw.20251059}
}