title: Gen-L-Video: Multi-Text to Long Video Generation via Temporal
    Co-Denoising
summary: 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.
paper_authors._id: 64757dd2a51711a3b591ad3d
paper_authors.name: Fu-Yun Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-30T09:01:41.902Z
paper_authors._id: 63e9e92f20c109718713f5eb
paper_authors.avatarUrl: /avatars/9ff312e854d803e1a2e9e685a21d12f8.svg
paper_authors.fullname: Fu-Yun Wang
paper_authors.type: user
paper_authors.user: wangfuyun
paper_authors._id: 64757dd2a51711a3b591ad3e
paper_authors.name: Wenshuo Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-30T10:08:06.881Z
paper_authors._id: 6475be9937ed88a749e63cb6
paper_authors.avatarUrl: /avatars/37df06697e8c4638e6f0ad49470dc4e3.svg
paper_authors.fullname: Wenshuo Chen
paper_authors.type: user
paper_authors.user: winshotchen
paper_authors._id: 64757dd2a51711a3b591ad3f
paper_authors.name: Guanglu Song
paper_authors._id: 64757dd2a51711a3b591ad40
paper_authors.name: Han-Jia Ye
paper_authors._id: 64757dd2a51711a3b591ad41
paper_authors.name: Yu Liu
paper_authors._id: 64757dd2a51711a3b591ad42
paper_authors.name: Hongsheng Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-05T10:51:10.529Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_title: Gen-L-Video: Multi-Text to Long Video Generation via Temporal
    Co-Denoising
paper_summary: 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.
paper_ai_summary: A novel paradigm extends short video diffusion models to handle long, multi-segment, text-conditioned video generation and editing without additional training.
paper_ai_keywords: diffusion models text-driven generative models image generation long videos semantic segments content consistency Gen-L-Video text-driven video generation text-driven video editing