Austrian Robotics Workshop

KINESIS: A Schema-Driven Motion Capturing, Management and Augmentation Framework

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Abstract

Motion capture systems are widely used to generate training data for machine
learning and robotics applications. However, existing workflows often rely on
fragmented toolchains, ad-hoc preprocessing scripts, and inconsistent data representations,
which complicates reproducibility and rapid experimentation.
We present Kinesis, an API-first schema-driven framework for capturing, validating,
inspecting, and augmenting motion data based on structured 3D keypoints.
The framework enforces consistent dataset structure through declarative skeletal
schemas, enables interactive inspection through synchronized tabular and 3D visualization,
and provides schema-aware augmentation operators for spatial and
temporal transformations.
To demonstrate practical applicability, we implement a WebXR-based hand gesture
capture pipeline using consumer-grade hardware (Meta Quest 3). While this setup
serves as a concrete example, the framework is hardware-agnostic and extensible
to other pose estimation sources. By unifying capture, validation, inspection,
and augmentation in a single system, Kinesis provides a reproducible foundation
for motion-based machine learning workflows and rapid prototyping in XR and
robotics scenarios.

How to Cite:

Günther, H., (2026) “KINESIS: A Schema-Driven Motion Capturing, Management and Augmentation Framework”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 69-72.