license: other
viewer: false
tags:
- deepfakes
- gen-ai
- text-to-video
pretty_name: DeepAction Dataset v1.0
size_categories:
- 1K<n<10K
task_categories:
- video-classification
The DeepAction dataset contains over 3,000 videos generated by seven text-to-video AI models, as well as real matched videos. These videos show people performing ordinary actions such as walking, running, and cooking. The AI models used to generate these videos include, in alphabetic order, AnimateDiff, CogVideoX5B, Lumiere, Pexels, RunwayML, StableDiffusion, Veo (pre-release version), and VideoPoet.
Licensing
TBD, will be provided by pcounsel
Getting Started
To get started, log into Hugging Face in your CLI environment, and run:
from datasets import load_dataset dataset = load_dataset("TBD_DATASET_ID", trust_remote_code=True)
Data
The data is structured into eight folders, corresponding to different text-to-video AI models. Each folder has 100 subfolders containing AI-generated videos. These subfolders correspond to action classes; all videos in a given subfolder were generated using the same prompt (see the list of prompts here).
|
Real: Scripted |
Real: Unscripted |
|
Real: Hand movement |
Real: Head movement |
|
Fake: Wav2Lip with real voice |
Fake: Wav2Lip with fake voice |
|
Fake: ReTalking with real voice |
Fake: ReTalking with fake voice |
|
Fake: Face Fusion |
Fake: Face Fusion + GAN |
|
Fake: Face Fusion Live |
Misc
Please use the following citation to refer to our work:
TBD
Matyas Bohacek, Google* and Stanford University Hany Farid, University of California, Berkeley
This work was done during the first author's (MB) internship at Google.