Chao Xu

I am a UCLA Ph.D. candidate, currently a visiting graduate at UC San Diego, fortunate to be advised by Prof. Hao Su. Previously, I received dual bachelor degrees in Computer Engineering from University of Illinois at Urbana-Champaign and Zhejiang University.

My research interests are focused on the fields of computer vision, robotics, and cognition. I actively engaged in pushing the boundaries of generalizable 3D vision: 1) object understanding, 2) 3D reconstruction and generation, etc.

I anticipate graduating in Spring 2025 and am on the job market, seeking full-time opportunities. Please feel free to contact me.

Email / CV / Google Scholar / Semantic Scholar / / GitHub

Research

	MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, Hongzhi Wu, Hao Su NeurIPS 2024 Oral Paper / Project / Demo A sparse-view reconstruction model that explicitly leverages 3D native structure, input guidance, and training supervision.
	SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views Chao Xu, Ang Li, Linghao Chen, Yulin Liu, Ruoxi Shi, Hao Su^†, Minghua Liu^† ECCV 2024 [also featured in CV4Metaverse (Oral) and Wild3D] Paper / Project / Demo Given sparse unposed views, we leverage rich priors embedded in multiview diffusion models to predict their poses and reconstruct the 3D shape.
	One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, Hao Su CVPR 2024* Paper / Project / Code Star / Demo We propose a new pipeline in the One-2-3-45's paradigm: More consistent multi-view generation (Zero123++) and better reconstruction (multi-view conditioned 3D native diffusion models).
	Zero123++: A Single Image to Consistent Multi-view Diffusion Base Model Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, Hao Su Technical Report Paper / Code Star We improve consistency and image conditioning in single-image multi-view generation.
	One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund V. T, Zexiang Xu, Hao Su NeurIPS 2023 Paper / Project / Code Star We rethink how to leverage 2D diffusion models for 3D AIGC and introduce a novel forward-only paradigm that avoids the time-consuming optimization.
	GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts Haoran Geng, Helin Xu, Chengyang Zhao, Chao Xu, Li Yi, Siyuan Huang, He Wang CVPR 2023* Highlight (10% of accepted, scores: 5, 5, 5) Paper / Project / Code We learn parts on articulated objects across categories.
	PartAfford: Part‑level Affordance Discovery from Cross‑category 3D Objects Chao Xu, Yixin Chen, He Wang, Song-Chun Zhu, Yixin Zhu, Siyuan Huang ECCV 2022 Visual Object-oriented Learning meets Interaction Workshop Paper / Video We discover part affordances on 3D objects across categories under weak supervision.

Modified from Jon Barron's.