About
Wenhao Chai
Docs (updated on 06/25/2025):
Curriculum Vitae
Research:
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GitHub
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Zhihu
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Xiaohongshu
Wenhao Chai is an incoming Ph.D. Student in Computer Science at Princeton University, working with Prof. Zhuang Liu. He received his master's degree at University of Washington in 2025 and bachelor's degree at Zhejiang University in 2023. He previously studied at Stanford University as a research intern in the summer of 2024 working with Prof. Christopher D. Manning and at the University of Illinois Urbana-Champaign as a visiting scholar in the spring and summer of 2022. He has internship at Pika Labs and Microsoft Research Asia. His research spans a wide range of topics in computer vision and machine learning. His previous research primarily covers video understanding and generative models. He leads the development of MovieChat, the first Large Mutli-Modal Model for hour-long video understanding. He has published research papers in top-tier conferences and journals such as ICLR, CVPR, ICCV, ECCV, ACL, IROS and AAAI. He has co-organized workshops and challenges on video understanding at CVPR 2024 and 2025.
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News and Highlights
- FAQ. To junior master/undergraduate students: if you would like to chat about life, career plan, or research ideas related to AI/ML. I will dedicate at least 30 mins every week for such meetings. I encourage students from underrepresented groups to reach out.
- Internship. We are looking for interns and visiting students at Princeton University. Please refer to the content in the link for more details. We welcome passionate individuals to join our research community—feel free to reach out if you have any questions!
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- 09/2025: I join Princeton University as a CS Ph.D. student, working with Prof. Zhuang Liu. 2025 Fall application Record.
- 06/2025: One paper accepted to ICCV 2025.
- 06/2025: Featured in MIT Technology Review as one of the lead authors of LiveCodeBench Pro.
- 06/2025: One paper accepted to IROS 2025.
- 05/2025: One paper accepted to ACL 2025.
- 04/2025: We host CVPR 2025 Video Understanding Challenge @ LOVEU sponsored by Lambda.
- 03/2025: I graduate from University of Washington with the Thesis of Large Multi-Model Models for Video Captioning.
- 02/2025: Three papers accepted to CVPR 2025.
- 01/2025: Two papers accepted to ICLR 2025.
- 12/2024: Two papers accepted to AAAI 2025.
- 07/2024: Two papers accepted to ECCV 2024.
- 06/2024: I work with Pika Labs as intern to develop next-generation video understanding and generation models.
- 04/2024: We host CVPR 2024 Long-form Video Understanding Challenge @ LOVEU.
- 04/2024: Invited talk at AgentX seminar about our STEVE series works.
- 02/2024: Two papers accepted to CVPR 2024 with one highlight (2.81%).
- 02/2024: Invited talk at AAAI 2024 workshop @ IMAGEOMICS.
- 12/2023: One paper accepted to AAAI 2024.
- 09/2023: One paper accepted to ICCV 2023 workshop @ TNGCV-DataComp.
- 07/2023: Two papers accepted to ICCV 2023.
Recent
Projects
* Equal contribution. † Project lead. ‡ Corresponding author.
LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
Zihan Zheng*†, Zerui Cheng*, Zeyu Shen*, Shang Zhou*, Kaiyuan Liu*, Hansen He*, Dongruixuan Li, Stanley Wei, Hangyi Hao, Jianzhu Yao, Peiyao Sheng, Zixuan Wang, Wenhao Chai†, Aleksandra Korolova, Peter Henderson, Sanjeev Arora, Pramod Viswanath, Jingbo Shang‡, Saining Xie‡
arXiv preprint, 2025
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MIT Technology Review
Models like o3-high, o4-mini, and Gemini 2.5 Pro score 0% on hard competitive programming problems.
AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark
Wenhao Chai†, Enxin Song, Yilun Du, Chenlin Meng, Vashisht Madhavan, Omer Bar-Tal, Jenq-Neng Hwang, Saining Xie, Christopher D. Manning
International Conference on Learning Representations (ICLR), 2025
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AuroraCap is a multimodal LLM designed for image and video detailed captioning. We also release VDC, the first benchmark for detailed video captioning.
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang, Jenq-Neng Hwang‡
arXiv preprint, 2024
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SAMURAI is a zero-shot visual tracking framework that adapts Segment Anything Model (SAM) for visual tracking with motion-aware memory.
MovieChat: From Dense Token to Sparse Memory in Long Video Understanding
Enxin Song*, Wenhao Chai*†, Guanhong Wang*, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, Gaoang Wang‡
Computer Vision and Pattern Recognition (CVPR), 2024
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MovieChat achieves state-of-the-art performace in extra long video (more than 10,000 frames) understanding by introducing memory mechanism.
StableVideo: Text-driven Consistency-aware Diffusion Video Editing
Wenhao Chai, Xun Guo‡, Gaoang Wang, Yan Lu
International Conference on Computer Vision (ICCV), 2023
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We tackle introduce temporal dependency to existing text-driven diffusion models, which allows them to generate consistent appearance for the new objects.
Explore
More Pages
PhD Application Record
25 Fall Computer Science.
CVPR 2025
Summary and schedule.
Junior FAQ
Questions and answers.