Cheng Guo

Ph.D. Student in Computer Science · Physical AI and Multimodal Sensing

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I am Cheng Guo, a Ph.D. student in Computer and Information Sciences at UNC Charlotte, advised by Prof. Hongfei Xue. Before joining UNC Charlotte, I received my Master’s degree in Information and Communication Engineering from Huazhong University of Science and Technology, advised by Prof. Fei Hu.

My research centers on Physical AI for robust and scalable perception in the real world. I develop learning- and simulation-based methods that allow intelligent systems to perceive humans, motion, and 3D scenes from radars and complementary sensors. My work spans radar-based human mesh and motion recovery, radar-camera perception, simulation-to-real transfer, and foundation models for sensor-based intelligence.

Research focus Radar perception Human motion and mesh recovery Simulation-to-real learning

Experience

News

Jul 27, 2026 Our paper Monocular Models are Strong Learners for Multi-View Human Mesh Recovery was accepted to ECCV 2026 as an Oral Presentation.
Jul 24, 2026 mmSimPrior is now available on arXiv.
Apr 30, 2025 One paper accepted at IEEE Transactions on Instrumentation and Measurement! :tada:
Oct 22, 2023 Awarded the China National Scholarship (Top 1%)! :1st_place_medal:
Mar 22, 2023 One paper accepted at IEEE Transactions on Instrumentation and Measurement! :tada:

Selected Publications

2026

  1. arXiv
    mmsimprior_overview.webp
    mmSimPrior: Learning Simulation Priors for Data-Efficient Real-World Generalizable Radar-Based Human Motion Reconstruction
    Cheng Guo, Qiming Cao, Shengkai Xu, and 4 more authors
    arXiv preprint arXiv:2607.22973, 2026
    arXiv Paper Project Page
  2. ECCV
    monocular_multiview_hmr.png
    Monocular Models are Strong Learners for Multi-View Human Mesh Recovery
    Haoyu Xie, Shengkai Xu, Cheng Guo, and 6 more authors
    In European Conference on Computer Vision, 2026
    ECCV 2026 Oral Presentation
    arXiv Paper

2025

  1. TIM
    PMMW_Fuzzy.jpg
    An Iterative Method Combining Fuzzy Fusion and Fisher Vectors for Concealed Object Detection in Passive Millimeter-Wave Imaging
    Bo Fang, Dong Zhu, Yayun Cheng, and 5 more authors
    IEEE Transactions on Instrumentation and Measurement, 2025
    Paper

2023

  1. TIM
    PMMW_DETR.jpg
    Concealed object detection for passive millimeter-wave security imaging based on task-aligned detection transformer
    Cheng Guo, Fei Hu, and Yan Hu
    IEEE Transactions on Instrumentation and Measurement, 2023
    Paper

Miscellaneous

Awards

  • China National Scholarship, 2023

Journal Reviewer

  • Information Fusion
  • IEEE Sensors Journal