Papers
3
Total Citations
25
H-Index
2
About
You Yang is a rising researcher at the intersection of computer vision, 3D scene understanding, and robotics. Their work focuses on enabling machines to perceive and interact with complex 3D environments, with key contributions in human attention inference, sparse scene completion, and articulated object manipulation. Yang’s 2021 paper on blind 3D human attention inference from a third-person perspective (13 citations) addresses a critical challenge for human-robot collaboration and autonomous driving by inferring object-wise attention without visible eyes. Their 2024 work, ESC-Net (10 citations), tackles the “triple sparsity” problem in 3D LiDAR point clouds for extreme sparse scene completion, a vital capability for low-cost mobile robot systems. Most recently, Yang’s 2025 paper, ArtGS (2 citations), introduces a novel framework extending 3D Gaussian Splatting to integrate visual and physical models for interactive manipulation of articulated objects, pushing the boundaries of robotic physical reasoning. With a clear trajectory from perception to physical interaction, Yang’s research is shaping how autonomous systems understand and act within dynamic 3D worlds, making their work highly relevant for students and researchers in embodied AI and robotics.
Research Focus
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Top Papers
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