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Xueyang Yu I am a first year PhD student at University of Massachusetts Amherst, working with Prof. Chuang Gan. Previously, I received my bachelor's degree in Computer Science from ShanghaiTech University under the supervision of Prof. Xuming He. I was also fortunate to be mentored by Prof. Yu-xiong Wang at UIUC and Dr. Yossi Gandelsman at BAIR during my undergraduate.
I am actively seeking for 27 Summer Research Internship, with interest in Multiodal and Embodied AI. Feel free to reach out if you have any opportunities! Email / Google Scholar / Twitter / Github / WeChat |
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Research(‡: Equal contribution) |
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Fast Spatial Memory with Scalable Elastic Test-Time Training
Ziqiao Ma‡, Xueyang Yu‡, Haoyu Zhen, Yuncong Yang, Joyce Chai, Chuang Gan ECCV 2026 Project | Paper | Code We propose Fast Spatial Memory (FSM), which leverages scalable elastic test-time training to enable efficient and high-quality 4D reconstruction from dynamic videos. |
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Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens
Zeyuan Yang‡, Xueyang Yu‡, Delin Chen, Maohao Shen, Chuang Gan CVPR 2026 ICCV 2025, KnowledgeMR workshop (Best Paper) Project | Paper | Code We propose Mirage, interleaving latent visual tokens, which represent compact imagery visual features, with explicit text tokens to solve diverse multimodal reasoning tasks, boosting the reasoning performance without the full pixel-level image generation. |
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TalkCuts: A Large-Scale Dataset for Multi-Shot Human Speech Video Generation
Jiaben Chen‡, Zixin Wang‡, Ailing Zeng, Yang Fu, Xueyang Yu , Siyuan Cen, Julian Tanke, Yihang Chen, Koichi Saito, Yuki Mitsufuji, and Chuang Gan NeurIPS 2025 Project | Paper | Code We present TalkCuts, a large-scale benchmark dataset designed to facilitate the study of multi-shot human speech video generation. |
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VCA: Video Curious Agent for Long Video Understanding
Zeyuan Yang‡, Delin Chen‡, Xueyang Yu, Maohao Shen, Chuang Gan ICCV 2025 Project | Paper We introduce VCA, a curiosity-driven video agent with self-exploration capability, which autonomously navigates video segments and efficiently builds a comprehensive understanding of complex video sequences. |
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GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
Lang Lin‡, Xueyang Yu‡, Ziqi Pang‡, Yu-xiong Wang CVPR 2025 Project | Paper | Code We propose a simple yet effective MLLMs for language-instructed video segmentation. It emphasizes global-local video understanding and achieves SOTA performance on multiple benchmarks. |
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Learning Video Representations
without Natural Videos
Xueyang Yu, Xinlei Chen, Yossi Gandelsman ICCV 2025, LIMIT workshop Project | Paper | Code We show that synthetic videos and natural images can replace real videos for pre-training, achieving comparable or better performance while offering a controllable, transparent alternative. |
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