LoVoRA: 基于文本引导和无掩码的视频对象移除与添加方法
LoVoRA: Text-guided and Mask-free Video Object Removal and Addition with Learnable Object-aware Localization
AI总结：
LoVoRA通过可学习的对象感知定位机制实现无掩码的视频对象移除与添加，结合图像到视频转换和光流传播，实现端到端视频编辑。
摘要：
基于文本引导的视频编辑，特别是对象移除和添加，仍然是一个具有挑战性的任务，因为需要精确的空间和时间一致性。现有方法通常依赖于辅助掩码或参考图像进行编辑引导，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，一种新的框架，利用对象感知定位机制实现无掩码的视频对象移除和添加。我们的方法利用一个独特的数据集构建流程，整合了图像到视频的转换、基于光流的掩码传播和视频修复，使时间一致的编辑成为可能。LoVoRA的核心创新是其可学习的对象感知定位机制，该机制为对象插入和移除任务提供了密集的时空监督。通过利用扩散掩码预测器，LoVoRA实现了端到端的视频编辑，无需在推理过程中使用外部控制信号。广泛的实验和人类评估证明了LoVoRA的有效性和高质量性能。
英文摘要：
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. https://cz-5f.github.io/LoVoRA.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
