Leveraging large-scale image-text datasets and advancements in diffusion
models, text-driven generative models have made remarkable strides in the field
of image generation and editing. This study explores the potential of extending
the text-driven ability to the generation and editing of multi-text conditioned
long videos. Current methodologies for video generation and editing, while
innovative, are often confined to extremely short videos (typically less than
24 frames) and are limited to a single text condition. These constraints
significantly limit their applications given that real-world videos usually
consist of multiple segments, each bearing different semantic information. To
address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video,
capable of extending off-the-shelf short video diffusion models for generating
and editing videos comprising hundreds of frames with diverse semantic segments
without introducing additional training, all while preserving content
consistency. We have implemented three mainstream text-driven video generation
and editing methodologies and extended them to accommodate longer videos imbued
with a variety of semantic segments with our proposed paradigm. Our
experimental outcomes reveal that our approach significantly broadens the
generative and editing capabilities of video diffusion models, offering new
possibilities for future research and applications. The code is available at
this https URL

本文提出了一种称为 Gen-L-Video 的新方法，该方法利用现有的短视频扩散模型扩展了当前的文本驱动视频生成和编辑能力，使其能够生成和编辑具有多个语义段的数百帧的长视频，而不需要额外的训练，从而极大地拓宽了视频扩散模型的生成和编辑能力。