Ziteng Cui

Ziteng Cui (崔子藤)

I am a specially appointed assistant professor (特任助教) at the University of Tokyo, affiliated with the MIL Lab at RCAST. I received my Ph.D. from the University of Tokyo, M.S. from Shanghai Jiao Tong University, and B.S. from Harbin Institute of Technology.

I mainly work on Vision Robustness & Computational Photography & 3D Computer Vision. My favorite things are DOTA2, hiking, anime (JOJO, One Piece, HunterxHunter ...) and Pink Floyd.

I'm always open to academic collaborations and have lots of interesting ideas I'd love to explore, feel free to reach out if you'd like to work together.

Email / Google Scholar / Github / Twitter / LinkedIn

昔者庄周梦为胡蝶，栩栩然胡蝶也，自喻适志与，不知周也。俄然觉，则蘧蘧然周也。不知周之梦为胡蝶与？胡蝶之梦为周与？周与胡蝶，则必有分矣，此之谓物化。 -- 庄子《齐物论》

News

● 2025.6.01 I successfully defended my Ph.D. dissertation, Dr. Cui now 💐💐
● 2025.4.12 I have been selected to the Doctoral Consortium at CVPR 2025 ☀️☀️
● 2025.2.27 One first author paper Luminance-GS has been accepted by CVPR 2025 ☀️☀️
● 2024.7.01 One first author paper RAW-Adapter has been accepted by ECCV 2024 ☀️☀️
● 2023.12.09 One first author paper Aleth-NeRF has been accepted by AAAI 2024 ☀️☀️
● ... ...

Selected Publications

I'm now interested in the physics modeling of low-level vision and 3D computer vision, I especially interested in the neural radiance field. "*" means authors contribute equally. Full publication list please refer to here.

	Luminance-GS: Adapting 3D Gaussian Splatting to Challenging Lighting Conditions with View-Adaptive Curve Adjustment Ziteng Cui, Xuangeng Chu, Tatsuya Harada. CVPR, 2025 [github] website / arxiv / bibtex / poster A simple multi-view curve adjustment method for novel view synthesis under challenging lighting conditions, including low-light, overexposure, and varying exposure.
	ARTalk: Speech-Driven 3D Head Animation via Autoregressive Model Xuangeng Chu, Nabarun Goswami, Ziteng Cui, Hanqin Wang, Tatsuya Harada. Arxiv, 2025 [github] website / arxiv / bibtex ARTalk generates realistic 3D head motions (lip sync, blinking, expressions, head poses) from audio in real-time.
	Discovering an Image-Adaptive Coordinate System for Photography Processing Ziteng Cui, Lin Gu, Tatsuya Harada. BMVC, 2024 arxiv / bibtex Instead of just using image-adaptive curves or 3D LUTs, why not design an image-adaptive coordinate system tailored to different photography processing tasks?
	RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images Ziteng Cui, Tatsuya Harada. ECCV, 2024 [github] website / arxiv / bibtex / poster We analyze the relationship between camera RAW data and sRGB images pre-trained models, and propose RAW-Adapter for effective RAW data based vision tasks.
	Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption Ziteng Cui, Lin Gu, Xiao Sun, Xianzheng Ma, Yu Qiao, Tatsuya Harada. AAAI, 2024 [github] website / arxiv / arxiv (old version) / bibtex / poster Blight NeRF's volume rendering function with concealing fields, to handle novel view synthesis under low-light conditions and overexposure conditions.
	Monodetr: Depth-guided transformer for monocular 3d object detection Renrui Zhang, Han Qiu, Tai Wang, Ziyu Guo, Ziteng Cui, Yu Qiao, Hongsheng Li, Peng Gao ICCV, 2023 [github] arxiv / bibtex / code / poster MonoDETR introduces a depth-guided transformer for monocular 3D object detection, enhancing Mono3D with non-local depth cues and achieving SOTA results.
	You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction Ziteng Cui, Kunchang Li, Lin Gu, Shenghan Su, Peng Gao, Zhengkai Jiang, Yu Qiao, Tatsuya Harada. BMVC, 2022 [github] arxiv / bibtex / demo / poster A super light-weight (only 90k+ parameters) transformer-based network Illumination Adaptive Transformer, for real time image enhancement and exposure correction.
	Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection Ziteng Cui, Yingying Zhu, Lin Gu, Guo-Jun Qi, Xiaoxiao Li, Renrui Zhang, Zenghui Zhang, Tatsuya Harada. ECCV, 2022 [github] arxiv / bibtex / poster Combine detection with self-supervised super-resolution, for robust detection under various degradation conditions (noise, blurry, low-resolution).
	Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection Ziteng Cui, Guo-Jun Qi, Lin Gu, Shaodi You, Zenghui Zhang, Tatsuya Harada. ICCV, 2021 [github] arxiv / bibtex / poster Using Camera-ISP pipeline for low-light image synthetic, then using self-supervised learning to improving the performance of low-light condition object detection.