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• Stable Video Object Removal (SVOR) is a robust framework designed to handle real-world imperfections in video object removal, including shadows, abrupt motion, and defective masks.method 95%
• Mask Union for Stable Erasure (MUSE) is a windowed union strategy used during temporal mask downsampling to preserve target regions and handle abrupt motion, reducing missed removals.method 95%
• Denoising-Aware Segmentation (DA-Seg) is a lightweight segmentation head on a decoupled side branch that provides an internal diffusion-aware localization prior without impacting content generation.method 95%
• The proposed Curriculum Two-Stage Training involves self-supervised pretraining on unpaired real-background videos followed by refinement on synthetic pairs with mask degradation and side-effect-weighted losses.method 95%
• SVOR achieves new state-of-the-art results across multiple datasets and degraded-mask benchmarks.result 95%
• SVOR improves cross-domain robustness by jointly removing objects and their associated shadows/reflections.result 90%
• Existing diffusion-based video inpainting models struggle with temporal stability and visual consistency under real-world imperfections.limitation 90%

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