Weijie Wang   |   王伟杰

I am a first-year Ph.D. student in Computer Science at College of Computer Science and Technology, Zhejiang University, advised by Prof. Bohan Zhuang. My research interests lie in the fields of efficient large 3D model/world model. Now, I am a research intern at at Microsoft Research Asia. Previously, I obtained my B.E. degree from College of Computer Science and Technology and Chu Kochen Honors College (ACEE) at Zhejiang University in 2025. During my undergraduate study, I was fortunate to work with Prof. Xiaowei Zhou, Prof. Sida Peng, Prof. Huajun Chen, Prof. Keyan Ding and Dr. Zheng Zhu.

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We introduce PM-Loss, a novel regularization loss based on a learned point map for feed-forward 3DGS, leading to smoother 3D geometry and better rendering.

ZPressor is an architecture-agnostic module that compresses multi-view inputs for scalable feed-forward 3DGS.

We introduce WonderTurbo, the first real-time interactive 3D scene generation framework capable of generating novel perspectives of 3D scenes within 0.72 seconds.

We propose a single-view RGB-D-based depth completion framework, TransDiff, that leverages the Denoising Diffusion Probabilistic Models(DDPM) to achieve material-agnostic object grasping in desktop.

This paper aims to tackle the problem of modeling dynamic urban street scenes from monocular videos. We introduce Street Gaussians, a new explicit scene representation that tackles some major limitations.

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