Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling

We introduce OpenStory++, a large-scale open-domain dataset focusing on enabling MLLMs to perform storytelling generation tasks.

TODOs

• Release dataset
• Release dataset organization code
• Release data process pipeline code
• Release video preprocessing code
• Release benchmark evaluation code

Dataset Organization

1. You can use img2dataset to organize single image dataset

   example:

   img2dataset --url_list OpenstoryPlusPlus/unique_v2/part1 --input_format "parquet" --url_col "url" --output_format webdataset --output_folder "single_tar" --processes_count 12 --thread_count 12 --save_additional_columns '["png","json"]'  --image_size 512 --resize_mode="keep_ratio" --enable_wandb False

2. To organize the story dataset as described in the paper, you can use utilis\download_videos.py to download videos from YouTube and extract frames for the dataset. Additionally, you can utilize utilis\organize_story.py to properly structure the story dataset.

Dataset Process Pipeline

1. You can use single_pipeline.py to get images with instance-level annotation.

   Hint: the input image should in webdataset format, the source image should in the "jpg" key.