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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. To bridge this fundamental gap in video editing, we propose …