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.
Abstract (translated)
从视频中移除对象在面对现实世界中的不完美情况（如阴影、突然移动和缺陷遮罩）时仍然颇具挑战。现有的基于扩散的视频修复模型通常难以在这种情况下保持时间稳定性和视觉一致性。为此，我们提出了SVOR（Stable Video Object Removal），这是一种稳健框架，通过三个关键设计实现无影、无闪烁且能够容忍遮罩缺陷的移除： 1. **Mask Union for Stable Erasure (MUSE)**：这是一种在时序掩码下采样过程中应用的窗口化联合策略，用于保存每个窗口内观察到的所有目标区域。这种方法有效地处理了突然移动并减少了遗漏的移除情况。 2. **Denoising-Aware Segmentation (DA-Seg)**：这是轻量级分割头，连接在一个解耦的侧支上，并装备有Denoising-Aware AdaLN，在使用遮罩退化进行训练的情况下提供内部扩散感知定位先验，同时不影响内容生成。 3. **Curriculum Two-Stage Training**：第一阶段（Stage I）执行未配对的真实背景视频上的自我监督预训练，并在线随机应用掩码以学习真实的背景和时间先验。第二阶段（Stage II）使用遮罩退化及副作用加权损失，在合成对上进行精炼，同时去除对象及其相关的阴影/反射，并提高跨域鲁棒性。 广泛的实验表明，SVOR在多个数据集和遮罩降级基准测试中达到了新的最先进水平，从而将视频对象移除从理想设置推进到了现实世界的应用场景。
URL
https://arxiv.org/abs/2603.09283
