Ruijie Xu

Shanghai, China

I am Ruijie Xu, a Ph.D. student in the DMCV Lab at East China Normal University, advised by Prof. Xin Tan, and a Research Intern at the Safe and Trustworthy AI Center, Shanghai AI Laboratory.

My research focuses on embodied AI, 3D vision, and interactive scene understanding, with an emphasis on controllable 3D scene authoring, scene editing, and trustworthy vision-language model evaluation. Recent work includes MUSE, JointEdit3D, and TSHA.

Before joining ECNU, I received my B.Sc. in Computer Science and Technology from Chongqing University in 2026. I also maintain awesome-ai2-thor and welcome research collaborations in embodied AI and 3D vision.

news

Jul 09, 2026	Invited to review for The Visual Computer and npj Heritage Science.
Jun 11, 2026	Our preprint MUSE, a memory-grounded multi-agent framework for controllable 3D scene authoring, is now available.
Jun 10, 2026	Our preprint JointEdit3D introduces feed-forward 3D scene editing in a unified RGB-geometry latent space.
Mar 30, 2026	Our TSHA benchmark for trustworthy safety hazard assessment with vision-language models is now available.
Jul 08, 2025	Our work OSH-Splat was published in The Visual Computer.

selected publications

arXiv
MUSE: Agentic 3D Scene Authoring via Memory-Grounded Incremental Requirement Satisfaction

Ruijie Xu, Xinnan Zhu, Jiayu Ying, Daoguo Dong, Yuzhou Ji, and Xin Tan

arXiv preprint arXiv:2606.14168Ruijie Xu and Xinnan Zhu contributed equally. , 2026

Abs arXiv Bib Website

Text-driven 3D scene generation is promising for digital content creation, embodied AI simulation, and interactive design, yet practical workflows require refining existing scenes while preserving non-target content. MUSE formulates controllable 3D scene authoring as incremental requirement satisfaction and introduces a memory-grounded multi-agent framework with an Architect, Sculptor, and Inspector. It also introduces AuthorBench for requirement-level controllability and preservation-aware editing.
@article{xu2026muse, title = {MUSE: Agentic 3D Scene Authoring via Memory-Grounded Incremental Requirement Satisfaction}, author = {Xu, Ruijie and Zhu, Xinnan and Ying, Jiayu and Dong, Daoguo and Ji, Yuzhou and Tan, Xin}, journal = {arXiv preprint arXiv:2606.14168}, year = {2026}, }
arXiv
JointEdit3D: Feed-Forward 3D Scene Editing in a Unified Latent Space

Xinnan Zhu, Ruijie Xu, Jiayu Ying, Daoguo Dong, Jiachen Xu, Yuan Xie, and 1 more author

arXiv preprint arXiv:2606.13345Xinnan Zhu and Ruijie Xu contributed equally. , 2026

Abs arXiv Bib Website

JointEdit3D performs feed-forward 3D scene editing in a unified RGB-geometry latent space. It uses asymmetric latent inpainting and a SceneAnchor Branch to propagate an edit from one reference frame while preserving source-scene structure. The work also introduces the SceneEdit3D-15K paired dataset and the SceneEdit3D-Bench evaluation benchmark.
@article{zhu2026jointedit3d, title = {JointEdit3D: Feed-Forward 3D Scene Editing in a Unified Latent Space}, author = {Zhu, Xinnan and Xu, Ruijie and Ying, Jiayu and Dong, Daoguo and Xu, Jiachen and Xie, Yuan and Tan, Xin}, journal = {arXiv preprint arXiv:2606.13345}, year = {2026}, }
Vis. Comput.
OSH-Splat: Optimizable Semantic Hyperplanes for Enhanced 3D Language Feature Gaussian Splatting

Ruijie Xu, Yuzhou Ji, Xin Tan, and Lizhuang Ma

The Visual Computer, 2025

Abs DOI Bib

OSH-Splat constructs a compact 3D language field for accurate and efficient open-vocabulary queries. It extracts hierarchical semantic information at part, subpart, and whole levels, learns compact semantic features on 3D Gaussians, and optimizes a semantic hyperplane for each text query to improve localization accuracy and robustness.
@article{xu2025oshsplat, title = {OSH-Splat: Optimizable Semantic Hyperplanes for Enhanced 3D Language Feature Gaussian Splatting}, author = {Xu, Ruijie and Ji, Yuzhou and Tan, Xin and Ma, Lizhuang}, journal = {The Visual Computer}, volume = {41}, number = {13}, pages = {11127--11137}, year = {2025}, doi = {10.1007/s00371-025-04091-5}, }