EVE: Efficient zero-shot text-based Video Editing with Depth Map
  Guidance and Temporal Consistency Constraints
Yutao Chen Xingning Dong Tian Gan Chunluan Zhou Ming Yang Qingpei Guo
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.