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README.md

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+# 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 haoningwu3639@gmail.com or liuchang666@sjtu.edu.cn.

StorySalon.zip

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