Hi, thanks for stopping by! I am now a third-year Ph.D. Student at The University of North Carolina at Chapel Hill, advised by Prof. Mohit Bansal. Previously, I did my undergraduate study at Shanghai Jiao Tong University.

I also work at Amazon (2023) / Adobe Research (2024) / Google Deepmind (2025).

My research focuses on multimodal AI, with a particular emphasis on video-centric AI modeling.

I develop and enhance models/systems capable of effectively and efficiently perceiving and inferring from the dynamic and diverse visual world. My work aims to enable AI to assist humans in understanding complex video content for advanced reasoning and manipulation, contributing to a broad spectrum of downstream applications (sports, security, medical, and educational domains), and fostering the development of more adaptable and intelligent video-based AI systems. They are:

Find me here: shoubin -atsign- cs . unc . edu

πŸ”₯ News

  • 2025.03: πŸ₯¦ VEGGIE is on arXiv.
  • 2025.02: πŸ’¬ Gave an invited talk at Twelve Labs.
  • 2025.02: πŸ‘€ 2 papers accepted to CVPR 2025. Check VideoTree for dynamic/adaptive keyframe selection with LLM, GroundMoRe for a new motion-grounded video reasoning task.
  • 2025.02: 🧠 Will summer intern at Google Deepmind.
  • 2025.01: πŸ‡ΈπŸ‡¬ 3 papers accepted to ICLR 2025. Check β˜•CREMA for video+any modality reasoning, SAFREE for training-free safe visual generation, and ⛓️SRDF for human-level VL-Navigation.
  • 2024.09: πŸ““ 1 paper accepted to EMNLP 2024. Check LLoVi for long VideoQA with LLM.
  • 2024.07: πŸ“Ή 1 paper accepted to ACMMM 2024. Check IVA-0 for controllable image animation.
  • 2024.06: πŸ’¬ Gave an invited talk at Google.
  • 2024.05: 🎬 Summer intern at Adobe.
  • 2023.09: ⛓️ 1 paper accepted to NeurIPS 2023. Check SeViLA for Video Loc+QA.
  • 2023.07: 🦴 1 paper accepted to IEEE TCSVT. Check MoPRL for skeletal anomaly detection.
  • 2023.05: 🌞 Summer intern at Amazon.
  • 2022.09: β›ͺ️ Join UNC-CH MURGe-Lab .
  • 2022.06: πŸŽ“ Graduate from Shanghai Jiao Tong University (outstanding graduates).
  • 2021.10: 🌟 1 paper accepted to NeurIPS 2021. Check STAR for real-world situated reasoning.

πŸ“ Pre-print (*: equal contribution/co-first author)

Preprint
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VEGGIE: Instructional Editing and Reasoning of Video Concepts with Grounded Generation

Shoubin Yu*, Difan Liu*, Ziqiao Ma*, Yicong Hong, Yang Zhou, Hao Tan, Joyce Chai, Mohit Bansal

Code | Project Page

  • We propose VEGGIE, a unified and versatile video generative model that handles various tasks for both video concept grounding and editing according to user instructions.
Preprint
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RACCooN: Remove, Add, and Change Video Content with Auto-Generated Narratives

Jaehong Yoon*, Shoubin Yu*, Mohit Bansal

Code | Project Page

  • We present RACCooN, a versatile and user-friendly video-to-paragraph-to-video framework, enables users to remove, add, or change video content via updating auto-generated narratives.

πŸ“ Publications

CVPR 2025
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Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level

Andong Deng, Tongjia Chen, Shoubin Yu, Taojiannan Yang, Lincoln Spencer, Yapeng Tian, Ajmal Saeed Mian, Mohit Bansal, Chen Chen

Code | Project Page

  • We present GroundMoRe, a new benchmark for novel Motion-Grounded Video Reasoning, designed to assess multimodal models’ reasoning and perception capabilities for motion understanding.
CVPR 2025
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VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos

Ziyang Wang*, Shoubin Yu*, Elias Stengel-Eskin*, Jaehong Yoon, Feng Cheng, Gedas Bertasius, Mohit Bansal

Code | Project Page

  • We present VideoTree, an adaptive tree-based video presentation/prompting with simple visual clustering for long video reasoning with LLM.
ICLR 2025
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SAFREE: Train-free And Adaptive Guard For Safe Text-to-Image And Video Generation

Jaehong Yoon*, Shoubin Yu*, Vaidehi Patil, Huaxiu Yao, Mohit Bansal

Code | Project Page

  • We propose SAFREE, a concept guard that can zero transfer to any visual diffusion models for safe generation.
ICLR 2025
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CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion

Shoubin Yu*, Jaehong Yoon*, Mohit Bansal

Code | Project Page

  • We present CREMA, an efficient & modular modality-fusion framework for injecting any new modality into video reasoning.
ICLR 2025
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Bootstrapping Language-guided Navigation Learning with Self-refining Data Flywheel

Zun Wang, Jialu Li, Yicong Hong, Songze Li, Kunchang Li, Shoubin Yu, Yi Wang, Yu Qiao, Yali Wang, Mohit Bansal, Limin Wang

Code

  • We present a Self-Refining Data Flywheel strategy for VLN, surpassing/approaching human performance on several benchmarks.
EMNLP 2024
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A Simple LLM Framework for Long-Range Video Question-Answering

Ce Zhang, Taixi Lu, Md Mohaiminul Islam, Ziyang Wang, Shoubin Yu, Mohit Bansal, Gedas Bertasius

Code

  • We present LLoVi, a simple yet effective framework with LLM for long-range video question-answering.
ACM MM 2024
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Zero-Shot Controllable Image-to-Video Animation via Motion Decomposition

Shoubin Yu, Jacob Zhiyuan Fang, Skyler Zheng, Gunnar Sigurdsson, Vicente Ordonez, Robinson Piramuthu, Mohit Bansal

Code | Homepage

  • We present IVA-0, a Image-to-Video animator, enables precise control from users through in-place and out-of-place motion decomposition.
NeurIPS 2023
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Self-Chained Image-Language Model for Video Localization and Question Answering

Shoubin Yu, Jaemin Cho, Prateek Yadav, Mohit Bansal

Code | Demo | Talk

  • We propose SeViLA, which self-chained BLIP-2 for 2-stage video question-answering (localization + QA) & refine localization with QA feedback.
TCSVT 2023
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Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

Shoubin Yu, Zhongyin Zhao, Haoshu Fang, Andong Deng, Haisheng Su, Dongliang Wang, Weihao Gan, Cewu Lu, Wei Wu

Code

  • We propose MoPRL, a transformer-based model incorporated with skeletal motion prior for efficient video anomaly detection.
NeurIPS 2021
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STAR: A Benchmark for Situated Reasoning in Real-World Videos

Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B. Tenenbaum, Chuang Gan

Code | Project Page

  • We propose STAR, a benchmark for neural-symbolic video reasoning in real-world scenes.

πŸŽ– Honors and Awards

  • Piepieβ€˜s (1-year-old black Shiba-Inu 🐢) Dad, 2024
  • The Hui-Chun Chin and Tsung Dao Lee Scholar, 2020
  • Meritorious Award in Mathematical Contest in Modeling, 2019
  • Second Prize in Shanghai, China Undergraduate Mathematical Contest in Modeling, 2019

🧐 Service

  • Conference reviewer: CVPR, ECCV, NeurIPS, ICLR, ICML, AISTATS, ARR (ACL, EMNLP, CoNLL, NACCAL, EACL), AAAI
  • Journal reviewer: IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Multimedia (TMM)

πŸ“– Educations

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  • 2022.09 - Present
  • The University of North Carolina at Chapel Hill
  • Computer Science, Ph.D.
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  • 2017.09 - 2022.06
  • Shanghai Jiao Tong University
  • Information Security, B.Eng.

πŸ’» Internships

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