SignMate

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 continues her exploration of motion-capture tools by testing Plask and FreeMoCap to understand how well they can track ASL gestures. She compares their strengths, limitations, and the challenges they present when used for sign-language animation.

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.

In today’s demo, Munkhchimeg Sergelen explains how American Sign Language (ASL) differs from spoken English and why those differences play a critical role in designing SignMate.

Join us inside the Lightbloom Lab in Budapest as Mugi walks through her latest experiment — attempting to adapt our teammate Kevin into a real-time signing avatar.

Meet Mugi, one of our brilliant AI researchers in the Budapest Lab. She is building SignMate to make communication more accessible through speech to sign avatars.