EVE：基于深度图引导与时序一致性约束的高效零样本文本驱动视频编辑
EVE: Efficient zero-shot text-based Video Editing with Depth Map Guidance and Temporal Consistency Constraints
AI总结：
针对现有文本驱动视频编辑方法微调成本高、生成能力有限且缺乏公开基准的问题，提出带深度图引导与时序一致性约束的零样本编辑方法EVE，构建ZVE-50数据集，实现性能与效率的良好权衡并将开源相关资源。
摘要：
受图像扩散模型优异性能的推动，越来越多研究者致力于将这类模型拓展至文本驱动视频编辑任务。然而，当前视频编辑任务主要面临微调成本高与生成能力有限的两难困境。与图像相比，我们推测视频在编辑过程中需要更多约束以维持时序一致性。为此，我们提出EVE，一种鲁棒且高效的零样本视频编辑方法。在深度图引导与时序一致性约束的作用下，EVE以可承受的计算与时间成本取得了令人满意的视频编辑效果。此外，鉴于目前缺乏用于公平比较的公开视频编辑数据集，我们构建了一个新的基准数据集ZVE-50。通过全面实验，我们验证了EVE能够在性能与效率之间实现令人满意的权衡。我们将公开该数据集与代码库，为后续研究者提供便利。
英文摘要：
Motivated by the superior performance of image diffusion models, more and more researchers strive to extend these models to the text-based video editing task. Nevertheless, current video editing tasks mainly suffer from the dilemma between the high fine-tuning cost and the limited generation capacity. Compared with images, we conjecture that videos necessitate more constraints to preserve the temporal consistency during editing. Towards this end, we propose EVE, a robust and efficient zero-shot video editing method. Under the guidance of depth maps and temporal consistency constraints, EVE derives satisfactory video editing results with an affordable computational and time cost. Moreover, recognizing the absence of a publicly available video editing dataset for fair comparisons, we construct a new benchmark ZVE-50 dataset. Through comprehensive experimentation, we validate that EVE could achieve a satisfactory trade-off between performance and efficiency. We will release our dataset and codebase to facilitate future researchers.
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。
