Ishaan Shah

Indian Institute of Technology Hyderabad

Papers

Total Citations

H-Index

About

Ishaan Shah is a rising researcher in computer vision and graphics, with a focus on human-object interaction and 3D reconstruction. His work centers on developing markerless methods to capture and model how hands manipulate objects—a critical challenge for advancing robotics, mixed reality, and digital human animation. Shah’s most notable contribution is his pioneering paper, "MANUS: Markerless Grasp Capture Using Articulated 3D Gaussians" (2024), which introduces a novel framework that uses articulated 3D Gaussians to accurately model hand-object contact without requiring physical markers or specialized hardware. This work has already garnered 17 citations in its first year, signaling strong early impact in the field. By enabling precise, markerless capture of complex grasps, Shah’s research bridges the gap between realistic hand modeling and practical applications in teleoperation and virtual environments. His approach represents a significant step forward from traditional skeleton- or mesh-based methods, offering a more flexible and accurate representation of contact dynamics. As a young researcher, Shah is quickly establishing himself as a key voice in the intersection of 3D vision and human-centered AI.