Hi!

Hi! I’m Baoxiong, a research scientist at BIGAI. I received my Ph.D in the Department of Computer Science, University of California, Los Angeles. My research interests lie in the intersection of computer vision, artificial intelligence and cognitive science, with a special focus on spatial/temporal reasoning and its application to acting and planning in real world (scene/activity understanding, future prediction, grounded manipulation, etc.). Previously, I obtained my M.S. from UCLA in 2019 and B.S. from Peking University in 2018.

Info: Email / Google Scholar / CV

News

  •   New        Our embodied generalist LEO is accepted by ICML 2024. Check out our code and data at this link .
  •   New        I'm co-organizing the MANGO workshop at CVPR 2024. See you in Seattle!
  •   New        SceneVerse data is released ! Find the download link and instructions at this link .
  •   New        Two papers on 3D motion and scene generation accepted by CVPR 2024 as Highlight .
  • 2024/03    LEO code and data is released ! Find the download link and instruction at this link .
  • 2024/02    Announcing SceneVerse for 3D-VL learning. Checkout our the project page .
  • 2023/12    One paper on procedural understanding in videos is accepted by NeurIPS 2023.
  • 2023/10    Two papers accepted by ICCV 2023, congrats to the authors!
  • 2023/10    One paper on temporal and causal transition of objects is accepted by IROS 2023.
  • 06/2023    One paper on diffusion models for 3D is accepted by CVPR 2023.
  • 05/2023    One paper on unsupervised object-centric learning is accepted by ICLR 2023.
  • 12/2022    One paper on language grounding for robots is accepted as spotlight by LangRob@CoRL 2022.
  • 10/2022    EgoTaskQA project page released, checkout our code and data!
  • 09/2022    One paper on egocentric goal-oriented reasoning is accepted by NeurIPS 2022.
  • 05/2022    One paper on systematic generalization in RAVEN test is accepted by ECCV 2022.
  • 04/2022    One paper on latent diffusion energy-based model is accepted by ICML 2022.
  • Recent Selected Publications (All publications)

    An Embodied Generalist Agent in 3D World

    International Conference on Machine Learning (ICML) 2024
    GenAI4DM & AGI @ ICLR 2024 (* indicates equal contribution.)

    Human-level Few-shot Concept Induction through Minimax Entropy Learning

    Chi Zhang , Baoxiong Jia , Yixin Zhu , Song-Chun Zhu .
    Science Advances (SciAdv) 2024

    PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI

    , Baoxiong Jia* , , Siyuan Huang .
    Conference on Computer Vision and Pattern Recognition (CVPR) 2024 (Highlight)
    (* indicates equal contribution.)

    Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance

    Zan Wang , Yixin Chen , Baoxiong Jia , Puhao Li , Jinlu Zhang , , Tengyu Liu , Yixin Zhu , Wei Liang , Siyuan Huang .
    Conference on Computer Vision and Pattern Recognition (CVPR) 2024 (Highlight)

    SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding

    Preprint

    ARNOLD: A Benchmark for Language-Grounded Task Learning with Continuous States in Realistic Scenes

    Ran Gong* , Jiangyong Huang* , Yizhou Zhao , Haoran Geng , Xiaofeng Gao , , , , Demetri Terzopoulos , Song-Chun Zhu , Baoxiong Jia , Siyuan Huang .
    International Conference on Computer Vision (ICCV) 2023
    LangRob @ CoRL 2022 (* indicates equal contribution.)

    Diffusion-based Generation, Optimization, and Planning in 3D Scenes

    Conference on Computer Vision and Pattern Recognition (CVPR) 2023
    (* indicates equal contribution.)

    Improving Unsupervised Object-centric Learning with Query Optimization

    Baoxiong Jia* , Yu Liu* , Siyuan Huang .
    International Conference on Learning Represetnations (ICLR) 2023
    (* indicates equal contribution.)

    EgoTaskQA: Understanding Human Tasks in Egocentric Videos

    Baoxiong Jia , , Song-Chun Zhu , Siyuan Huang .
    Advances in Neural Information Processing System (NeurIPS) 2022 (Track on Datasets and Benchmarks)


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