Angela Xing

Papers

Total Citations

H-Index

About

Angela Xing is a leading researcher in computer vision and graphics, with a focus on reconstructing and modeling dexterous human-object interactions. Her work tackles the fundamental challenge of understanding how hands grasp objects—a problem critical to advancing robotics, mixed reality, and human-computer interaction. Xing’s most notable contribution is the paper "MANUS: Markerless Grasp Capture Using Articulated 3D Gaussians" (2024, 17 citations), which introduces a novel approach to capturing hand-object grasps without the need for physical markers or cumbersome sensors. By leveraging articulated 3D Gaussians, MANUS achieves highly accurate modeling of hand-object contact, surpassing prior methods that rely on skeletons, meshes, or parametric models. This innovation enables more realistic and robust grasp reconstruction, directly impacting the development of intuitive robotic manipulation and immersive virtual environments. Xing’s work stands out for its elegant fusion of geometric and learning-based techniques, offering a scalable solution to a long-standing problem. With growing recognition in the field, her research continues to shape how machines perceive and replicate human dexterity.