Suzanne Sorli

Papers

Total Citations

127

H-Index

About

Suzanne Sorli is a leading researcher in computer vision and human-computer interaction, with a primary focus on 3D hand pose estimation and reconstruction from monocular RGB video. Her most significant contribution is the development of **RGB2Hands**, a pioneering framework that tackles the extremely challenging problem of tracking and reconstructing the 3D pose and geometry of two interacting hands in real time—a task critical for applications in augmented/virtual reality, robotics, and sign language recognition. Prior to her work, existing methods were largely limited to simpler, single-hand tracking or required depth sensors. Sorli’s approach demonstrated that robust, real-time two-hand tracking is achievable using only a standard RGB camera, overcoming severe challenges like heavy occlusion and self-similarity between hands. Her seminal paper on the topic has garnered **85 citations**, with a follow-up journal version adding another **42 citations**, underscoring the high relevance and impact of her work. By enabling more natural and intuitive hand-based interfaces, Sorli’s research directly advances the frontier of immersive technology and interactive systems.