Shengqu Cai 「蔡盛曲」

I'm a 1st-year CS PhD student at Stanford University, advised by Prof. Gordon Wetzstein and Prof. Leonidas Guibas, affliated with Computational Imaging Lab and Geometric Computing Lab. I am partly supported by a Stanford School of Engineering Fellowship.

Before Stanford, I was a CS master student at ETH Zürich supervised by Prof. Luc Van Gool. I obtained my Bachelor degree in Computer Science with first honour from King's College London in United Kingdom, where I spent some time working on information theory.

In 2022, I spent a few wonderful months working on scene extrapolation with Eric Chan and Songyou Peng. I started my research career back in 2021 working on NeRFs and GANs with Anton Obukhov. I consider them as my mentors and who I try my best to learn from.

I am interested in solving graphics or inverse graphics tasks that are fundamentally ill-posed via traditional methods, slay the unslayable. I have been working primarily around neural rendering and generative models, including but not limited to diffusion models, inverse rendering, unsupervised learning methods, scene representations, etc. I like making cool theories, videos, demos and applications.

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* This is me prior-COVID. Since then I gained >40 pounds and lost my cool
  • 2024-02: Generative Rendering is accepted to CVPR 2024, see you in Seattle!
  • 2023-09: I joined Stanford University for PhD in Computer Science!
  • 2023-07: DiffDreamer is accepted by ICCV 2023, looking forward to Paris!
  • 2023-05: I graduated from ETH Zürich!
  • 2023-01: I will be working as a research intern at Adobe this summer!
  • 2022-03: Pix2NeRF is accepted by CVPR 2022. First submission first accept!
  • 2022-03: Started my master thesis at Stanford University!

* indicates equal contribution

dise Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control
Zhengfei Kuang*, Shengqu Cai*, Hao He, Yinghao Xu, Hongsheng Li, Leonidas Guibas, Gordon Wetzstein
arXiv, 2024
[Project Page][Paper]

Multi-view/multi-trajectory generation of videos sharing the same underlying content and dynamics.

dise Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models
Shengqu Cai, Duygu Ceylan*, Matheus Gadelha*, Chun-Hao Paul Huang, Tuanfeng Y. Wang, Gordon Wetzstein
In CVPR, 2024
[Project Page][Paper]

Render low fidelity animated mesh directly into animation using pre-trained 2D diffusion models, without the need of any further training/distillation.

dise DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models
Shengqu Cai, Eric Ryan Chan, Songyou Peng, Mohamad Shahbazi, Anton Obukhov, Luc Van Gool, Gordon Wetzstein
In ICCV, 2023
[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
In CVPR, 2022

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


  • Conference Review: CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, Eurographics, SIGGRAPH
  • Journal Review: IJCV, Computing Surveys

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