Shengqu Cai

I am an incoming CS PhD student at Stanford University and a research scientist intern at Adobe Research.
Previously, I was a CS master student at ETH Zürich supervised by Prof. Luc Van Gool.
In 2022, I spent a wonderful half a year visiting Stanford University at the Computational Imaging Lab led by Prof. Gordon Wetzstein, working on scene extrapolation.
Before this, I obtained my Bachelor degree in Computer Science with first honour from King's College London in United Kingdom, working on unsupervised learning.

I am interested in using artificial intelligence to solve tasks that are fundamentally ill-posed via traditional methods, slay the unslayable. I have been working primarily around neural rendering and 3D vision, including but not limited to generative models, inverse rendering, unsupervised learning, GAN inversion, scene representations, visual content creation, etc.

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  • 2023-02: I am admitted to Stanford University for PhD in Computer Science!
  • 2023-01: I will be working as a research scientist intern at Adobe Research this summer!
  • 2022-03: Pix2NeRF is accepted by CVPR 2022. First submission first accept!
  • 2022-03: Started my master thesis at Stanford University. Looking forward to visiting bay area!
  • Publications

    * indicates equal contribution

    dise DiffDreamer: Consistent Single-view Perpetual View Generation with Conditional Diffusion Models
    Shengqu Cai, Eric Ryan Chan, Songyou Peng, Mohamad Shahbazi, Anton Obukhov, Luc Van Gool, Gordon Wetzstein
    Under review
    [Project Page][Paper][Code]

    A diffusion-model based unsupervised framework capable of synthesizing novel views depicting a long camera trajectory flying into an input image.

    dise Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation
    Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc Van Gool
    Conference on Computer Vision and Pattern Recognition (CVPR), 2022

    3D-free unsupervised Single view NeRF-based novel view synthesis via conditional NeRF-GAN training and inversion.


  • Conference Review: ECCV, CVPR, ICCV
  • Journal Review: IJCV

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