Gen-L-Video: Multi-Text to Long Video Generation via Temporal
  Co-Denoising
Fu-Yun Wang Wenshuo Chen Guanglu Song Han-Jia Ye Yu Liu Hongsheng Li
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
https://github.com/G-U-N/Gen-L-Video.