Austrian Robotics Workshop
Author: Hutter Günther (Montanuniversität Leoben)
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.
Keywords:
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). doi: https://doi.org/10.34749/3061-1466.2026.13