ThinkV2V2 min read
ThinkV2V: Unleashing the Reasoning Capability of MLLMs for Instruction-Guided Video Editing
Instruction-guided video editing has made significant progress, yet existing methods use multimodal large language models (MLLMs) primarily as semantic encoders, so they often fall short in working with implicit edits that require causal or semantic reasoning.
By Donghao Zhou, Haoyang He, Fan Zhang, Hao Yang, Guisheng Liu, Xin Gao, Zhongwei Wan, Xingyuan Bu, Jie Wang, Qiangpeng Yang, Shilei Wen, Chi-Wing Fu, Pheng-Ann HengarXiv - cs.CV (CV)
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Originally published by arXiv - cs.CV (CV) on Oct 1, 2026.
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