Long-form Video Prior 🎥

Learning Long-form Video Prior via Generative Pre-Training

Jinheng Xie¹  Jiajun Feng^1*  Zhaoxu Tian^1*  Kevin Qinghong Lin¹  Yawen Huang² Xi Xia¹  Nanxu Gong¹  Xu Zuo¹  Jiaqi Yang¹  Yefeng Zheng²  Mike Zheng Shou¹

¹ National University of Singapore  ² Jarvis Research Center, Tencent Youtu Lab 

Updates

• [2024-04-24] The dataset is released.

Storyboard20K dataset with fine-grained annotations

An overview of the proposed Storyboard20K. It includes around 20K storyboards sourced from MovieNet and LSMDC with around 150K shots (key frames) in total. For each storyboard, there are around 15.7 and 8.9 annotated bounding boxes for characters and film sets, respectively. The average duration of these movie storyboards is around 33.9 seconds, which ensures long-time coverage and large view variation.

Data samples

Annotated samples (part of a storyboard) of the proposed Storyboard20K. Our dataset involves three main annotations, i.e., (i) character-centric (whole body keypoints and bounding boxes), (ii) film-set-centric (bounding boxes), and (iii) summative (texts) annotations. It also includes condensed or shot-by-shot descriptions.

Download

The annotation can be downloaded from here and is structured as follows:

# each storyboard in train.json and test.json has the following elements
{   
    'flag': 'train' ('val', 'testA', or 'testB'),
    'global_id': ,
    'movie_id': ,
    'key_frames': ,
    'resolution': ,
    'title': ,
    'genre': ,
    'emotion': ,
    'scene': ,
    'summary': ,
    'cast': ,
    'main characters': ,
    '#characters': ,
    'synopses': ,
    
     # a list of N (#frames of the current storyboard) sub-lists and each sub-list contains M bounding boxes formatted in [x1, y1, x2, y2], 
     # in which each coordinate is scaled into [0,1] by dividing the long side of the frame (max(H,W)).
    'bboxes_person': ,
    'bboxes_object': ,
    'keypoints': ,
}

Visualize storyboards

python data_preprocess.py --input-path path/to/json/file --num-instructions 1 --vis-save-dir outputs/debug --instruct --save-flag instruct --vis-storyboard --max-frames 11 --noise

or

python data_preprocess.py --input-path path/to/json/file --num-instructions 1 --vis-save-dir outputs/debug --instruct --save-flag instruct --vis-storyboard --max-frames 11 --noise --data-root path/to/storyboard20k/frames/[train, test]

The visualized storyboards will be stored at outputs/debug. --noise means no random noises are added to each sample for augmentation. You can specify the root to source frames by --data-root to visualize storyboards with source frames. By specifying --vis-stroyboard, no text sequences will be saved. If you want to save the processed text sequences for training and test, you should run python data_preprocess.py --input-path path/to/json/file --num-instructions 1 --instruct --save-flag instruct without specifying --vis-storyboard.

Source Movie Frames

Please make sure to request access to the MPII Movie Description dataset (MPII-MD) first and cc the approval email to [email protected]. Once you have received approval, I will provide you with the link to download the pre-processed movie frames of Storyboard20K.

├── storyboard20k/ 
|   ├── frames
|   |   ├—— train
|   |   |   ├—— tt0167260
|   |   |   ├—— ...
|   └── ——— test

Citation

If you find our work inspiring or use our dataset or codebase in your research, please consider giving a star ⭐ and a citation.

@article{xie2024learning,
  title={Learning Long-form Video Prior via Generative Pre-Training},
  author={Xie, Jinheng and Feng, Jiajun and Tian, Zhaoxu and Lin, Kevin Qinghong and Huang, Yawen and Xia, Xi and Gong, Nanxu and Zuo, Xu and Yang, Jiaqi and Zheng, Yefeng and others},
  journal={arXiv preprint arXiv:2404.15909},
  year={2024}
}