Lucie Naert
Papers
2
Total Citations
15
H-Index
2
About
Lucie Naert is a leading researcher at the intersection of sign language linguistics and computer animation, specializing in the creation of expressive, data-driven signing avatars. Her primary research areas include motion synthesis, motion editing, and the development of high-quality motion capture corpora for French Sign Language (LSF). Naert’s major contribution lies in advancing beyond procedural, robotic animations—which are often rejected by the Deaf community—by leveraging captured human motion to generate natural, linguistically accurate signing. Her most cited work, "Motion synthesis and editing for the generation of new sign language content" (2021, 9 citations), introduces techniques to recombine and edit captured motion data, enabling the scalable production of novel signing content without sacrificing realism. She also co-created the "LSF-ANIMAL" corpus (2020, 6 citations), a unique motion capture dataset designed specifically for animating signing avatars, providing a foundational resource for the field. Through these efforts, Naert is helping to make sign language avatars a viable, accessible tool for deaf individuals to receive information in their preferred language, bridging a critical gap in digital inclusion.
Research Focus
Key Achievements
Top Papers
- 1
- 2