Why Noise-Free Data Matters for ASL Avatars

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 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.