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Motivated by the superior performance of image diffusion models, more andmore researchers strive to extend these models to the text-based video editingtask. Nevertheless, current video editing tasks mainly suffer from the dilemmabetween the high fine-tuning cost and the limited generation capacity. Comparedwith images, we conjecture that videos necessitate more constraints to preservethe temporal consistency during editing. Towards this end, we propose EVE, arobust and efficient zero-shot video editing method. Under the guidance ofdepth maps and temporal consistency constraints, EVE derives satisfactory videoediting results with an affordable computational and time cost. Moreover,recognizing the absence of a publicly available video editing dataset for faircomparisons, we construct a new benchmark ZVE-50 dataset. Through comprehensiveexperimentation, we validate that EVE could achieve a satisfactory trade-offbetween performance and efficiency. We will release our dataset and codebase tofacilitate future researchers.

EVE: Efficient zero-shot text-based Video Editing with Depth Map Guidance and Temporal Consistency Constraints