From Ideal to Real: Stable Video Object Removal under Imperfect Conditions

• URL: http://arxiv.org/abs/2603.09283v1
• Date: Tue, 10 Mar 2026 07:07:27 GMT
• Title: From Ideal to Real: Stable Video Object Removal under Imperfect Conditions
• Abstract summary: We propose a robust framework that achieves shadow-free, flicker-free, and mask-defect-tolerant removal through three key designs.<n>SVOR attains new state-of-the-art results across multiple datasets and degraded-mask benchmarks.
• Score: 20.340320193953865
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Removing objects from videos remains difficult in the presence of real-world imperfections such as shadows, abrupt motion, and defective masks. Existing diffusion-based video inpainting models often struggle to maintain temporal stability and visual consistency under these challenges. We propose Stable Video Object Removal (SVOR), a robust framework that achieves shadow-free, flicker-free, and mask-defect-tolerant removal through three key designs: (1) Mask Union for Stable Erasure (MUSE), a windowed union strategy applied during temporal mask downsampling to preserve all target regions observed within each window, effectively handling abrupt motion and reducing missed removals; (2) Denoising-Aware Segmentation (DA-Seg), a lightweight segmentation head on a decoupled side branch equipped with Denoising-Aware AdaLN and trained with mask degradation to provide an internal diffusion-aware localization prior without affecting content generation; and (3) Curriculum Two-Stage Training: where Stage I performs self-supervised pretraining on unpaired real-background videos with online random masks to learn realistic background and temporal priors, and Stage II refines on synthetic pairs using mask degradation and side-effect-weighted losses, jointly removing objects and their associated shadows/reflections while improving cross-domain robustness. Extensive experiments show that SVOR attains new state-of-the-art results across multiple datasets and degraded-mask benchmarks, advancing video object removal from ideal settings toward real-world applications.

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