TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for
  Image-to-Video Generation
Authors: name: Wenhao Wang, name: Yi Yang

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.