Abstract
Text-guided video editing, particularly for object removal and addition, remains a challenging task due to the need for precise spatial and temporal consistency. Existing methods often rely on auxiliary masks or reference images for editing guidance, which limits their scalability and generalization. To address these issues, we propose LoVoRA, a novel framework for mask-free video object removal and addition using object-aware localization mechanism. Our approach utilizes a unique dataset construction pipeline that integrates image-to-video translation, optical flow-based mask propagation, and video inpainting, enabling temporally consistent edits. The core innovation of LoVoRA is its learnable object-aware localization mechanism, which provides dense spatio-temporal supervision for both object insertion and removal tasks. By leveraging a Diffusion Mask Predictor, LoVoRA achieves end-to-end video editing without requiring external control signals during inference. Extensive experiments and human evaluation demonstrate the effectiveness and high-quality performance of LoVoRA.
Abstract (translated)
文本指导的视频编辑，特别是在对象移除和添加方面，由于需要精确的空间和时间一致性而仍然是一项具有挑战性的任务。现有方法通常依赖于辅助掩码或参考图像来进行编辑引导，这限制了它们的可扩展性和泛化能力。为了解决这些问题，我们提出了LoVoRA（Learnable Object-aware Localization for Video Remasking and Addition），这是一种新的无掩码视频对象移除和添加框架，采用感知物体定位机制。我们的方法利用了一个独特的数据集构建流水线，该流水线结合了图像到视频的转换、基于光流的掩码传播以及视频修复技术，从而实现时间一致性编辑。 LoVoRA的核心创新在于其可学习的对象感知定位机制，这种机制为对象插入和移除任务提供了密集的空间-时间监督。通过利用扩散掩码预测器（Diffusion Mask Predictor），LoVoRA能够实现端到端的视频编辑，并且在推理过程中不需要外部控制信号。大量的实验以及人类评估证明了LoVoRA的有效性和高质量性能。 该技术不仅解决了现有方法中依赖辅助信息的问题，还提高了视频编辑任务中的空间和时间一致性的准确性，为更广泛的视频编辑应用场景提供了可能。
URL
https://arxiv.org/abs/2512.02933
