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About this paper
Sen Liang, Fengbin Guan, Youliang Zhang, Xin Li, Zhibo Chen
CoT-Edit, a framework developed at the University of Science and Technology of China, introduces a "Plan–Guide–Edit" approach for instruction-based video editing, leveraging a Chain-of-Thought enhanced multimodal large language model to generate explicit spatial and semantic constraints. This method produces more precise, physically plausible, and temporally consistent video edits, demonstrating improved visual quality and instruction following compared to existing state-of-the-art methods.
Key insights
- Decomposing the complex instruction-based video editing task into distinct planning, guiding, and editing stages, especially by proactively generating explicit spatial and semantic priors via an MLLM, improves precision and plausibility.
- Leveraging Chain-of-Thought reasoning within the MLLM Planner enables robust interpretation of complex instructions and inferring physically plausible spatial and temporal dynamics.
- Bidirectional collaboration between the mask generation (Guide) and content editing (Editor) branches enhances mask stability, improves boundary details, and ensures better spatiotemporal consistency.
Summary by alphaXiv.
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