Yana Hasson

Papers

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About

Yana Hasson is a leading researcher in computer vision and 3D reconstruction, specializing in the intricate domain of hand-object interaction. Her work addresses a fundamental challenge: reconstructing the precise 3D geometry of hands and the objects they manipulate from standard RGB videos, without the need for specialized sensors. Her most-cited paper, “Towards Unconstrained Joint Hand-Object Reconstruction From RGB Videos” (2021, 57 citations), introduces a novel supervised learning framework that jointly estimates hand pose and object shape from monocular footage. This breakthrough is pivotal for advancing robotics, particularly in learning from human demonstrations, and for enabling immersive augmented reality experiences. By tackling the problem of occlusion and dynamic motion, Hasson’s research provides a pathway to more natural human-robot interaction and digital twin creation. Her work has been recognized for its potential to bridge the gap between visual perception and physical manipulation, earning her a reputation as a key innovator in 3D scene understanding. With a focus on unconstrained, real-world scenarios, Hasson continues to push the boundaries of what is possible in reconstructing complex, dynamic interactions from minimal input data.