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Xiang (Ryan) Li
My name is Xiang Li (李想, pronounced as Shiang Li). I am a fourth-year Ph.D. student at the University of Illinois Urbana-Champaign (UIUC), advised by Prof. James Rehg. My research focuses on controllable generative AI, especially 3D generation.
Previously, I obtained my bachelor's degree at the Hong Kong University of Science and Technology (HKUST), where I was advised by Prof. Yu-Wing Tai and Prof. Chi-Keung Tang.
Email /
Google Scholar /
CV /
LinkedIn
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Symmetry Strikes Back: From Single-Image Symmetry Detection to 3D Generation
Xiang Li,
Zixuan Huang,
Anh Thai,
James M. Rehg
In CVPR, 2025
paper
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project page
We propose Reflect3D, a zero-shot single-image 3D reflection symmetry detector; we improve single-image 3D generation through symmetry-aware optimization.
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Video State-Changing Object Segmentation
Jiangwei Yu*,
Xiang Li*,
Xinran Zhao,
Hongming Zhang,
Yu-Xiong Wang
In ICCV, 2023
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project page
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dataset and code
We present a weakly-supervised Video State-Changing Object Segmentation (VSCOS) benchmark, revealing challenges in current VOS models for state-changing objects and introducing three solutions for improved state-changing object segmentation.
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YouTubePD: A Multimodal Benchmark for Parkinson's Disease Analysis
Andy Zhou*,
Samuel Li*,
Pranav Sriram*,
Xiang Li*,
Jiahua Dong*,
Ansh Sharma,
Yuanyi Zhong,
Shirui Luo,
Maria Jaromin,
Volodymyr Kindratenko,
George Heintz,
Christopher Zallek,
Yu-Xiong Wang
In NeurIPS Datasets and Benchmarks Track, 2023
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project page
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dataset
We introduce YouTubePD, the first public multimodal benchmark for Parkinson's Disease (PD) analysis, crowdsourced from existing YouTube videos featuring over 200 subjects.
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One-Shot Object Detection without Fine-Tuning
Xiang Li*,
Lin Zhang*,
Yau Pun Chen,
Yu-Wing Tai,
Chi-Keung Tang
arXiv, 2020
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project page
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code
We introduce a two-stage model and training strategies for one-shot object detection by integrating the metric learning with an anchor-free Faster R-CNN-style detection pipeline.
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FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
Xiang Li,
Tianhan Wei,
Yau Pun Chen,
Yu-Wing Tai,
Chi-Keung Tang
In CVPR, 2020
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dataset and code
A few-shot segmentation dataset containing 1000 varied and balanced object categories with pixelwise annotation of ground-truth segmentation.
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