---
license: openrail
viewer: false
tags:
- deepfakes
- gen-ai
- text-to-video
pretty_name: DeepAction Dataset v1.0
size_categories:
- 1K<n<10K
task_categories:
- video-classification
---

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<img src="https://data.matsworld.io/ucbresearch/deepaction.gif" style="width: 100%">

The DeepAction dataset contains over 3,000 videos generated by six 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, Pexels, RunwayML, StableDiffusion, Veo (pre-release version), and VideoPoet. Refer to our <a href=''>our pre-print</a> for details.

<br>

## Getting Started

To get started, log into Hugging Face in your CLI environment, and run:

```python
from datasets import load_dataset
dataset = load_dataset("faridlab/deepaction_v1", trust_remote_code=True)
```

<br>

## Data

The data is structured into seven folders, with six folders corresponding to text-to-video AI models and one folder for real videos. Each of these folders has 100 subfolders corresponding to human action classes. All videos in a given subfolder were generated using the same prompt (see the list of prompts <a href=''>here</a>).

Included below are example videos generated using the prompt "a person taking a selfie". Note that, since each text-to-video AI model generates videos with different ratios and resolutions, these videos were normalized 512x512.

<table class="video-table">
    <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/deepaction/Pexels.mp4" controls></video>
            <p style="text-align: center;">Real</p>
        </td>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/deepaction/BDAnimateDiffLightning.mp4" controls ></video>
            <p style="text-align: center;">AnimateDiff</p>
        </td>
    </tr>
    <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/deepaction/CogVideoX5B.mp4" controls></video>
            <p style="text-align: center;">CogVideoX5B</p>
        </td>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/deepaction/RunwayML.mp4" controls ></video>
            <p style="text-align: center;">RunwayML</p>
        </td>
    </tr>
  <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/deepaction/StableDiffusion.mp4" controls></video>
            <p style="text-align: center;">StableDiffusion</p>
        </td>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/deepaction/Veo.mp4" controls ></video> 
            <p style="text-align: center;">Veo (pre-release version)</p>
        </td>
    </tr>
    <tr>
      <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/deepaction/VideoPoet.mp4" controls></video>
            <p style="text-align: center;">VideoPoet</p>
        </td>
        <td style="width: 50%;">
          
        </td>
    </tr>
</table>

<br>

# Licensing

TBD, will be provided by pcounsel

<br>

## Misc

Please use the following citation when referring to this dataset:

```bib
TBD
```

This work was done during the first author's (Matyas Bohacek) internship at Google.