Pose Representations for Deep Skeletal Animation
Nefeli Andreou, Andreas Aristidou, Yiorgos Chrysanthou

TL;DR
This paper introduces a dual quaternion-based pose representation for deep skeletal animation that better captures motion nuances, reduces artifacts, and enables training across diverse skeletons without retargeting.
Contribution
It proposes a novel dual quaternion-based pose encoding that improves motion fidelity and robustness in deep character animation models.
Findings
Overcomes common motion artifacts
Enables training on diverse skeletons without retargeting
Produces smooth and natural poses
Abstract
Data-driven character animation techniques rely on the existence of a properly established model of motion, capable of describing its rich context. However, commonly used motion representations often fail to accurately encode the full articulation of motion, or present artifacts. In this work, we address the fundamental problem of finding a robust pose representation for motion modeling, suitable for deep character animation, one that can better constrain poses and faithfully capture nuances correlated with skeletal characteristics. Our representation is based on dual quaternions, the mathematical abstractions with well-defined operations, which simultaneously encode rotational and positional orientation, enabling a hierarchy-aware encoding, centered around the root. We demonstrate that our representation overcomes common motion artifacts, and assess its performance compared to other…
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Taxonomy
TopicsHuman Motion and Animation · Human Pose and Action Recognition · 3D Shape Modeling and Analysis
