AR Annotation Services for Gesture and Spatial AI

Augmented reality systems depend on precise annotation to understand human gestures, depth cues, and spatial context. Whether you are building gesture recognition models, mixed reality interfaces, or teleoperation tools, the stability of your AI outputs depends directly on the quality of the labels in your training data.

DataVLab provides AR annotation services for XR, robotics, and spatial AI teams. Our annotators label hand and body keypoints, gesture classes, object interactions, and temporal sequences across video and sensor data. We pay particular attention to occlusions, identity consistency, and multi frame tracking so models can follow hands and objects reliably over time.

Workflows are tailored to each project. We help you formalise gesture definitions, interaction types, and edge cases so that all annotators follow the same rules. Quality control combines multi step review, calibration rounds, and internal audits to keep datasets consistent across long recordings.

With these processes in place, AR products gain smoother gesture control, more stable overlays, and safer interaction with robots or industrial equipment.