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.

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

Research

	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.