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I am a final-year PhD candidate in Computer Science at Brown University and a member of Brown Visual Computing Group. I am very fortunate to have Professor James Tompkin as my advisor. I received my Master in Computer Science from Columbia University, advised by Professor Shuran Song and Professor Shih-Fu Chang. I completed my Bachelor in Computer Science at Fudan University. I was a visiting student at MIT EECS (CSAIL). I was a research intern at NVIDIA Research with Abhishek Badki, Hang Su, Orazio Gallo, at Meta Reality Labs with Numair Khan, Lei Xiao, Douglas Lanman. Email / CV / Google Scholar / Github / Linkedin / Twitter I am actively seeking full-time research positions and postdoctoral opportunities in industry labs, with flexibility to start at any time. If you think I might be a good fit, please feel free to reach out! |
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Yiqing Liang, Jielin Qiu, Wenhao Ding, Zuxin Liu, James Tompkin, Mengdi Xu, Mengzhou Xia, Zhengzhong Tu, Laixi Shi, Jiacheng Zhu Under Review, 2025 paper / data / code / bibtex We introduce MoDoMoDo, a systematic post-training framework for Multimodal LLM RLVR, featuring a rigorous data mixture problem formulation and benchmark implementation. |
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Wenyan Cong, Yiqing Liang, Yancheng Zhang, Ziyi Yang, Yan Wang, Boris Ivanovic, Marco Pavone, Chen Chen, Zhangyang Wang, Zhiwen Fan Under Review, 2025 paper / code / bibtex We present the first comprehensive benchmark for 3D end‑to‑end 3D geometric foundation models, covering five core tasks: sparse-view depth estimation, video depth estimation, 3D reconstruction, multi-view pose estimation, novel view synthesis, and spanning both standard and challenging out-of-distribution datasets. |
 
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Yiqing Liang, Abhishek Badki*, Hang Su*, James Tompkin, Orazio Gallo CVPR, 2025 Oral, Award Candidate (0.48%) paper / video / code / bibtex We present ZeroMSF, the first generalizable 3D foundation model that understands monocular scene flow for diverse real-world scenarios, utilizing our curated data receipe of 1M synthetic training samples. |
 
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Yiqing Liang, Mikhail Okunev, Mikaela Angelina Uy, Runfeng Li, Leonidas J. Guibas, James Tompkin, Adam Harley TMLR, 2025 paper / data / code / bibtex We present a benchmark of dynamic Gaussian Splatting methods for monocular view synthesis, combining existing datasets and a new synthetic dataset to provide standardized comparisons and identify key factors affecting efficiency and quality. |
 
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Yiqing Liang, Numair Khan, Zhengqin Li, Thu Nguyen-Phuoc, Douglas Lanman, James Tompkin, Lei Xiao CVPR, 2024, CV4MR WACV, 2025 paper / code / bibtex We propose GauFRe: a dynamic scene reconstruction method using deformable 3D Gaussians for monocular video that is efficient to train, renders in real-time and separates static and dynamic regions. |
 
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Yiqing Liang, Eliot Laidlaw, Alexander Meyerowitz, Srinath Sridhar, James Tompkin ICCV, 2023 paper / code / bibtex We present SAFF: a dynamic neural volume reconstruction of a casual monocular video that consists of time-varying color, density, scene flow, semantics, and attention information. |
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Zhecan Wang*, Haoxuan You*, Liunian Harold Li, Alireza Zareian, Suji Park, Yiqing Liang, Kai-Wei Chang, Shih-Fu Chang AAAI, 2022 paper / bibtex We propose a Scene Graph Enhanced Image-Text Learning (SGEITL) framework to incorporate visual scene graph in commonsense reasoning |
 
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Yiqing Liang, Boyuan Chen, Shuran Song ICRA, 2021 paper / video / code / bibtex We explicitly model scene priors using a confidence-aware semantic scene completion module to complete the scene and guide the agent's navigation planning. |
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Based on Jon Barron's template. |