Exploring ASL Motion Capture with Plask & FreeMoCap

In this demo, Mugi presents an important update to her project by transitioning from a single-camera setup to a dual-camera solution. By introducing a second viewpoint, the system can capture motion and spatial information more accurately, reducing ambiguity and improving overall reliability.

In this session, Munkhchimeg Sergelen talks about how avatars are created for SignMate and why the underlying motion data must be extremely clean. ASL relies on precise handshapes and movement paths, so noisy or unrefined tracking can quickly break the accuracy of the sign.

In this demo, Mugi walks through her early experiments in capturing and generating ASL motion for SignMate. She explores several prototype approaches, including MediaPipe pose tracking, RPM, and ASL Mocap datasets, evaluating how each tool handles the complexity of sign-language gestures.

This session focuses on how SignMate transforms ASL gloss into 3D avatar motion. Mugi walks through the motion-generation system, showing how the model interprets gloss and maps it to realistic signing gestures.