Dan Casas

Papers

Total Citations

127

H-Index

About

Dan Casas is a researcher specializing in computer vision and human motion capture, with a particular focus on 3D hand tracking and reconstruction from monocular video. His most notable contribution, **RGB2Hands**, addresses one of the field's most technically demanding challenges: reconstructing the full 3D pose and geometry of two interacting hands in real time using only a standard RGB camera. Published in 2020 and extended in 2021, this work has accumulated over 125 citations combined, reflecting its significant impact on the research community. Casas's work directly advances applications in augmented and virtual reality, human-computer interaction, robotics, and sign language recognition — domains where accurate, accessible hand tracking is critical. By overcoming limitations of prior methods that required specialized depth sensors or handled only single-hand scenarios, his research makes robust two-hand interaction tracking practical for real-world deployment. His ability to bridge the gap between theoretical reconstruction methods and real-time performance requirements demonstrates both technical depth and applied vision. For students and researchers working in AR/VR interfaces or gesture-based systems, Casas's contributions represent a meaningful step forward in making natural human motion an accessible input modality.