TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation

• URL: http://arxiv.org/abs/2411.04709v1
• Date: Tue, 05 Nov 2024 18:52:43 GMT
• Title: TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation
• Authors: Wenhao Wang, Yi Yang,
• Abstract summary: TIP-I2V is the first large-scale dataset of user-provided text and image prompts for image-to-video generation. We provide the corresponding generated videos from five state-of-the-art image-to-video models.
• Score: 22.782099757385804
• License:
• Abstract: 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.

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