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. I am currently a research intern at ByteDance Seed.

Previously, I was a research intern at Microsoft Research Asia. I obtained my B.E. degree from College of Computer Science and Technology and Chu Kochen Honors College (ACEE) at Zhejiang University in 2025. I was fortunate to work closely with Prof. Chunhua Shen, Dr. Donny Y. Chen, Dr. Jiawang Bian and Dr. Zheng Zhu.

If you are interested in working with us as an intern at Zhejiang University, please feel free to email me and CC Prof. Bohan Zhuang.

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

VolSplat improves multi-view consistency and geometric accuracy for feed-forward 3DGS with voxel-aligned prediction.

We present DriveGen3D, a novel framework for generating high-quality and highly controllable dynamic 3D driving scenes that addresses critical limitations in existing methodologies.

We introduce MV-RoboBench, a benchmark for evaluating multi-view spatial reasoning in robotic manipulation, revealing large gaps between state-of-the-art VLMs and human performance.

We introduce ReVisual-R1, achieving a new state-of-the-art among open-source 7B MLLMs on challenging benchmarks.

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.

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.

This template is a modification to Jon Barron's website.