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I'm a 1st-year CS PhD student at Stanford Computer Science and the Computational Imaging Lab,
advised by/rotating with Prof. Gordon Wetzstein and Prof. Leonidas Guibas.
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.
In 2022, I spent a wonderful half a year working on scene extrapolation with Eric Chan and Songyou Peng.
I started my research career back in 2021 by working on single-view novel view synthesis 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, including but not limited to generative models, inverse rendering, unsupervised learning, scene representations, etc. I like making cool theory, videos and demos. Email / CV / Google Scholar / Semantic Scholar / Github / Twitter / Linkedin |
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* indicates equal contribution |
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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. |
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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. |
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Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc Van Gool In CVPR, 2022 [Paper][Code] 3D-free unsupervised Single view NeRF-based novel view synthesis via conditional NeRF-GAN training and inversion. |
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