Enhance dataset card: Add project page, abstract, and `library_name` metadata (#2)

- Enhance dataset card: Add project page, abstract, and `library_name` metadata (8de652697eaee7e0275f9563bdb6a4066dc77bef)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -9,6 +9,8 @@ task_categories:
 - text-to-video
 - text-to-image
 - image-to-image
+pretty_name: TIP-I2V
+library_name: datasets
 dataset_info:
   features:
   - name: UUID
@@ -52,14 +54,18 @@ tags:
 - text-to-video
 - visual-generation
 - video-generation
-pretty_name: TIP-I2V
 ---
 
 # Summary
 This is the dataset proposed in our paper [**TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation**](https://arxiv.org/abs/2411.04709).
 
+[Project page](https://tip-i2v.github.io/) | [Paper](https://arxiv.org/abs/2411.04709)
+
 TIP-I2V is the first dataset comprising over 1.70 million unique user-provided text and image prompts. Besides the prompts, TIP-I2V also includes videos generated by five state-of-the-art image-to-video models (Pika, Stable Video Diffusion, Open-Sora, I2VGen-XL, and CogVideoX-5B). The TIP-I2V contributes to the development of better and safer image-to-video models.
 
+# 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 available at this https URL .
+
 <p align="center">
   <img src="https://huggingface.co/datasets/WenhaoWang/TIP-I2V/resolve/main/assets/teasor.png" width="1000">
 </p>
@@ -197,7 +203,6 @@ hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="eval_videos_tar/cog_vide
 Click the [WizMap (TIP-I2V VS VidProM)](https://poloclub.github.io/wizmap/?dataURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_vidprom.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fgrid_tip-i2v_vidprom.json) and [WizMap (TIP-I2V VS DiffusionDB)](https://poloclub.github.io/wizmap/?dataURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_diffusiondb.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fgrid_tip-i2v_diffusiondb.json)
 (wait for 5 seconds) for an interactive visualization of our 1.70 million prompts.
 
-
 # License
 
 The prompts and videos in our TIP-I2V are licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en).