title: TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for
    Image-to-Video Generation
summary: Video generation models are revolutionizing content creation, with
  image-to-video models drawing increasing attention due to their enhanced
  controllability, visual consistency, and practical applications. However,
  despite their popularity, these models rely on user-provided text and image
  prompts, and there is currently no dedicated dataset for studying these
  prompts. In this paper, we introduce TIP-I2V, the first large-scale dataset of
  over 1.70 million unique user-provided Text and Image Prompts specifically for
  Image-to-Video generation. Additionally, we provide the corresponding generated
  videos from five state-of-the-art image-to-video models. We begin by outlining
  the time-consuming and costly process of curating this large-scale dataset.
  Next, we compare TIP-I2V to two popular prompt datasets, VidProM
  (text-to-video) and DiffusionDB (text-to-image), highlighting differences in
  both basic and semantic information. This dataset enables advancements in
  image-to-video research. For instance, to develop better models, researchers
  can use the prompts in TIP-I2V to analyze user preferences and evaluate the
  multi-dimensional performance of their trained models; and to enhance model
  safety, they may focus on addressing the misinformation issue caused by
  image-to-video models. The new research inspired by TIP-I2V and the differences
  with existing datasets emphasize the importance of a specialized image-to-video
  prompt dataset. The project is publicly available at https://tip-i2v.github.io.
paper_authors._id: 672d6e8447fe02e684b48e26
paper_authors.name: Wenhao Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:32:24.075Z
paper_authors._id: 62b32a4429a410b7f6b06710
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62b32a4429a410b7f6b06710/VzgvmnlYZWuifZTkIkCxy.jpeg
paper_authors.fullname: Wenhao Wang
paper_authors.type: user
paper_authors.user: WenhaoWang
paper_authors._id: 672d6e8447fe02e684b48e27
paper_authors.name: Yi Yang
paper_title: TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for
    Image-to-Video Generation
paper_summary: Video generation models are revolutionizing content creation, with
  image-to-video models drawing increasing attention due to their enhanced
  controllability, visual consistency, and practical applications. However,
  despite their popularity, these models rely on user-provided text and image
  prompts, and there is currently no dedicated dataset for studying these
  prompts. In this paper, we introduce TIP-I2V, the first large-scale dataset of
  over 1.70 million unique user-provided Text and Image Prompts specifically for
  Image-to-Video generation. Additionally, we provide the corresponding generated
  videos from five state-of-the-art image-to-video models. We begin by outlining
  the time-consuming and costly process of curating this large-scale dataset.
  Next, we compare TIP-I2V to two popular prompt datasets, VidProM
  (text-to-video) and DiffusionDB (text-to-image), highlighting differences in
  both basic and semantic information. This dataset enables advancements in
  image-to-video research. For instance, to develop better models, researchers
  can use the prompts in TIP-I2V to analyze user preferences and evaluate the
  multi-dimensional performance of their trained models; and to enhance model
  safety, they may focus on addressing the misinformation issue caused by
  image-to-video models. The new research inspired by TIP-I2V and the differences
  with existing datasets emphasize the importance of a specialized image-to-video
  prompt dataset. The project is publicly available at https://tip-i2v.github.io.
paper_ai_summary: TIP-I2V is a large-scale dataset of user-provided text and image prompts specifically for image-to-video generation, enabling advances in model development and safety.
paper_ai_keywords: image-to-video models text and image prompts TIP-I2V DiffusionDB VidProM user preferences model performance misinformation issue