通过缓解非预期变化来塑造稳定视频：面向概念增强的视频编辑
Shaping a Stabilized Video by Mitigating Unintended Changes for Concept-Augmented Video Editing
AI总结：
针对文本驱动视频编辑中开放概念编辑受限及非预期干扰问题，提出概念增强编辑方法，利用概念对和双先验监督，生成更稳定逼真的视频，超越现有方法。
摘要：
利用生成扩散模型进行文本驱动的视频编辑因其潜在应用而受到广泛关注。然而，现有方法受限于预训练中提供的有限词嵌入，这阻碍了针对具有特定属性的开放概念进行细致编辑。直接修改目标提示中的关键词往往会导致注意力机制的非预期干扰。为了实现更灵活的编辑，本工作提出了一种改进的概念增强视频编辑方法，通过设计抽象概念对来灵活生成多样且稳定的目标视频。具体而言，该框架包含概念增强的文本反转和双先验监督机制。前者能够实现对稳定扩散模型的即插即用式引导，用于视频编辑，有效捕获目标属性以获得更具风格化的结果。双先验监督机制显著增强了视频的稳定性和保真度。综合评估表明，我们的方法能够生成更稳定、更逼真的视频，优于现有最先进的方法。
英文摘要：
Text-driven video editing utilizing generative diffusion models has garnered significant attention due to their potential applications. However, existing approaches are constrained by the limited word embeddings provided in pre-training, which hinders nuanced editing targeting open concepts with specific attributes. Directly altering the keywords in target prompts often results in unintended disruptions to the attention mechanisms. To achieve more flexible editing easily, this work proposes an improved concept-augmented video editing approach that generates diverse and stable target videos flexibly by devising abstract conceptual pairs. Specifically, the framework involves concept-augmented textual inversion and a dual prior supervision mechanism. The former enables plug-and-play guidance of stable diffusion for video editing, effectively capturing target attributes for more stylized results. The dual prior supervision mechanism significantly enhances video stability and fidelity. Comprehensive evaluations demonstrate that our approach generates more stable and lifelike videos, outperforming state-of-the-art methods.
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。
