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• The proposed method introduces understanding into video object removal from two complementary perspectives: external distillation and internal framewise context cross-attention.method 95%
• Existing diffusion-based video object removal methods struggle to remove object-induced side effects like shadows, reflections, and illumination changes.limitation 90%
• The proposed method achieves state-of-the-art performance in video object removal.result 90%
• The paper establishes the first real-world benchmark for video object removal.technical 95%
• The external distillation scheme transfers relationships between objects and their induced effects from vision foundation models to video diffusion models.method 90%
• The framewise context cross-attention mechanism grounds each denoising block in informative, unmasked context surrounding the target region.method 90%
• The limitation of current video object removal methods stems from insufficient physical and semantic understanding of the target object and its interactions with the scene.limitation 85%
• External and internal guidance jointly enable the model to understand the target object, its induced effects, and the global background context.technical 90%

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