single-video-curation-svd

Educational repository for applying the main video data curation techniques presented in the Stable Video Diffusion paper. The goal is to provide an interactive resource for the community dig deeper into the curation techniques presented in the paper. As such, it doesn't guarantee precise correctness.

The curation techniques used in this repository are discussed in Appendix C of the paper, in detail. These include:

Cascaded Cut Detection
Keyframe-Aware Clipping
Optical Flow
Synthetic Captioning
Caption similarities and Aesthetics
Text Detection

The notebooks present these techniques on a single video file sourced from the UCF-101 dataset.

Video	CoCa Caption	V-BLIP Caption	Zephyr-7B Caption
	a group of men playing basketball on a court .	the basketball player dribbles to the basket	A basketball player in a group of men dribbles towards the basket on a court.

Readers are advised to first go through Appendix C of the Stable Video Diffusion paper before referring to the notebooks.

Setup

Below are the primary dependencies:

PyTorch (follow the installation instructions from the official site)
transformers
opencv-python
numpy
ffmpeg

Other dependencies will be detailed below.

Notebooks (should be run in order)

Clip extraction

Refer to the video_preprocessing_clip_extraction.ipynb notebook for this. You'd need to install the scenedetect library from here: https://github.com/Breakthrough/PySceneDetect. This shows both cascaded cut detection and keyframe-aware clipping. At the end of the notebook, you should expect to see different clips extracted from the provided video.

Captioning

video_preprocessing_captioning.ipynb presents synthetic captioning from a single video clip.

This uses three models:

CoCa (relies on open_clip)
V-BLIP (relies on EILEV)
Zephyr-7B (relies on transformers)

We had to apply some corrections to eilev to make it work. The correction patch can be found here.

Optical Flow

video_preprocessing_optical_flow_score.ipynb notebook shows the optical flow score computation only using the Farneback algorithm. It doesn't, however, show RAFT.

Caption similarities and Aesthetics

This is straightforward and is implemented in the video_preprocessing_similarity_aesthetics.ipynb notebook.

Text Detection

Refer to the video_preprocessing_text_detection.ipynb notebook for this. We use a wrapper library called craft_text_detector (repository) for this as it provides a handy package around the CRAFT text detection model. However, to make it work, we had to do some changes. The patch can be found here.

Acknowledgements

Thanks to ChatGPT for all the help.

Thanks to Dhruv Nair for his reviews.

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