From Ideal to Real: Stable Video Object Removal under Imperfect Conditions
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) Denoi...
| Authors | Jiagao Hu, Yuxuan Chen, Fuhao Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 March 2026 |
| Last updated at source | 22 April 2026 |
Models and datasets referencing this paper
- HigherHu/SVORModel
- HigherHu/RORD-50Dataset
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