Lightweight Morphology-Aware Encoding for Motion Learning
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Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present a lightweight method for encoding, learning, and predicting 3D rigged character motion sequences that consider both the character's pose and morphology. Specifically, we introduce an enhanced skeletal embedding that extends the standard skeletal representation by incorporating the radius of proxy cylinders, which conveys geometric information about the character's morphology at each joint. This additional geometric data is represented using compact tokens designed to work seamlessly with transformer architectures. This simple yet effective representation demonstrated through three distinct tokenization strategies, maintains the efficiency of skeletal-based representations while enhancing the accuracy of motion sequence predictions across diverse morphologies. Notably, our method achieves these results despite being trained on a limited dataset, showcasing its potential for applications with scarce animation data.
Description
CCS Concepts: Computing methodologies → Animation
@inproceedings{10.2312:egs.20251048,
booktitle = {Eurographics 2025 - Short Papers},
editor = {Ceylan, Duygu and Li, Tzu-Mao},
title = {{Lightweight Morphology-Aware Encoding for Motion Learning}},
author = {Wu, Ziyu and Michel, Thomas and Rohmer, Damien},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-268-4},
DOI = {10.2312/egs.20251048}
}