| # Intelligent Grimm - Open-ended Visual Storytelling via Latent Diffusion Models (CVPR 2024) | |
| This is the StorySalon dataset proposed in StoryGen. | |
| For the open-source PDF data, you can directly download the frames, corresponding masks, descriptions and original story narratives. | |
| For the data extracted from YouTube videos, we also provide their corresponding masks, descriptions and original story narratives in this repostiroy. However, you need to refer to `./Image_Inpainted/Video/metadata.json` to download the video meta-data by yourself, and then use the provided data processing pipeline to obtain the frames. | |
| ## Video Meta Data Preparation | |
| We provide the metadata of our StorySalon dataset in `./Image_Inpainted/Video/metadata.json`. It includes the id, name, url, duration and the keyframe list after filtering of the videos. | |
| To download these videos, we recommend to use [youtube-dl](https://github.com/yt-dlp/yt-dlp) via: | |
| ``` | |
| youtube-dl --write-auto-sub -o 'file\%(title)s.%(ext)s' -f 135 [url] | |
| ``` | |
| The keyframes extracted with the following data processing pipeline (step 1) can be filtered according to the keyframe list provided in the metadata to avoid manually selection. | |
| The corresponding masks, story-level description and visual description can be extracted with the following data processing pipeline or downloaded from [here](https://huggingface.co/datasets/haoningwu/StorySalon). | |
| ## Data Processing Pipeline | |
| The data processing pipeline includes several necessary steps: | |
| - Extract the keyframes and their corresponding subtitles; | |
| - Detect and remove duplicate frames; | |
| - Segment text, people, and headshots in images; and remove frames that only contain real people; | |
| - Inpaint the text, headshots and real hands in the frames according to the segmentation mask; | |
| - (Optional) Use Caption model combined with subtitles to generate a description of each image. | |
| For a more detailed introduction to the data processing pipeline, please refer to [StoryGen](https://github.com/haoningwu3639/StoryGen) and our paper. | |
| ## Citation | |
| If you use this dataset for your research or project, please cite: | |
| @inproceedings{liu2024intelligent, | |
| title = {Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models}, | |
| author = {Chang Liu, Haoning Wu, Yujie Zhong, Xiaoyun Zhang, Yanfeng Wang, Weidi Xie}, | |
| booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| year = {2024}, | |
| } | |
| ## Contact | |
| If you have any question, please feel free to contact [email protected] or [email protected]. | |