activity
20232026
most citedDepthMaster: Taming Diffusion Models for Monocular Depth Estimation

7 citations · 20 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CV2026

ReFlow: Self-correction Motion Learning for Dynamic Scene Reconstruction

Yanzhe Liang, Ruijie Zhu, Hanzhi Chang +3

We present ReFlow, a unified framework for monocular dynamic scene reconstruction that learns 3D motion in a novel self-correction manner from raw video. Existing methods often suf…

cs.CV2026

Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels

Jiahao Lu, Jiayi Xu, Wenbo Hu +5

Estimating the 3D trajectory of every pixel from a monocular video is crucial and promising for a comprehensive understanding of the 3D dynamics of videos. Recent monocular 3D trac…

cs.CV2026

SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization

Lifan Wu, Ruijie Zhu, Yubo Ai +1

4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit de…

cs.GR2025

MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting

Hanzhi Chang, Ruijie Zhu, Wenjie Chang +5

Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the…

cs.GR2025

ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting

Ruijie Zhu, Mulin Yu, Linning Xu +5

3D Gaussian Splatting is renowned for its high-fidelity reconstructions and real-time novel view synthesis, yet its lack of semantic understanding limits object-level perception. I…

cs.CV2025★ 7 cited

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Ziyang Song, Zerong Wang, Bo Li +5

Monocular depth estimation within the diffusion-denoising paradigm demonstrates impressive generalization ability but suffers from low inference speed. Recent methods adopt a singl…