---
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
---

<style>
        * {
        font-family: Helvetica, sans-serif;
          }
        code {
            font-family: IBM Plex Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,monospace !important;
        }
        a {
        color: #FFA500;
        }
        .container {
            display: flex;
            justify-content: space-between; /* Ensures even space between items */
            align-items: stretch; /* Ensures boxes have the same height */
            width: 100%;
            margin: 20px auto;
            gap: 20px; /* Consistent gap between boxes */
        }
        .warning-box {
            background-color: rgba(255, 200, 100, 0.5); /* Lighter orange with more translucency */
            border-radius: 10px;
            padding: 20px;
            flex: 1;
            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
            font-family: Arial, sans-serif;
            color: #333;
            display: flex;
            flex-direction: column;
            justify-content: flex-start; /* Align items to the top */
        }
        .warning-sign {
            font-weight: bold;
            font-size: 1em;
            margin-bottom: 10px;
        }
        .warning-text {
            font-size: 1em;
        }
        .button {
            display: inline-block;
            padding: 10px 20px;
            margin: 5px;
            background-color: #FFA500;
            color: white;
            text-decoration: none;
            border-radius: 5px;
        }
        .button span {
            margin-right: 10px;
        }
        .button:hover {
            background-color: #E69500;
        }
        .warning {
            background-color: rgba(255, 165, 0, 0.2);
            border-left: 5px solid #FFA500;
            border-radius: 5px;
            padding: 10px;
            margin: 10px 0;
            color: #000 !important;
        }
        .warning .title {
            color: #FFA500;
            font-weight: bold;
            display: flex;
            align-items: center;
        }
        .warning .title span {
            margin-right: 10px;
        }

.warning-banner {
    display: flex;
    align-items: center;
    justify-content: start; /* Adjusted to align content to the start */
    background-color: #FFCC80; /* Adjusted to a darker shade of orange for better contrast */
    color: #333;
    padding: 10px 30px;
    border-radius: 8px;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); /* Lighter shadow for subtlety */
    margin: 20px auto;
    width: 95%; /* Adjust width as needed */
    font-family: Helvetica, sans-serif;
}

.warning-icon {
    font-size: 1.5em;
    margin-right: 15px;
    color: #E65100; /* Darker orange for the icon */
}

.warning-message {
    font-size: 1em;
    font-weight: bold;
    flex: 1; /* Ensures message uses available space */
}

.warning-link {
    color: #0056b3; /* Standard link color for visibility */
    text-decoration: none; /* Removes underline */
}

.warning-link:hover {
    text-decoration: underline; /* Adds underline on hover for better interaction */
}
    </style>


<img src="tbd" style="width: 100%">

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.

<br>

<br>

# Licensing

TBD, will be provided by pcounsel

<br>

# 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)

<br>

<br>

## 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).

<table class="video-table">
    <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-real-scripted.mp4" controls></video>
            <p style="text-align: center;"><b>Real: </b> Scripted</p>
        </td>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-real-unscripted.mp4" controls ></video>
            <p style="text-align: center;"><b>Real: </b> Unscripted</p>
        </td>
    </tr>
    <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-real-hand-movement.mp4" controls></video>
            <p style="text-align: center;"><b>Real: </b> Hand movement</p>
        </td>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-real-head-movement.mp4" controls ></video>
            <p style="text-align: center;"><b>Real: </b> Head movement</p>
        </td>
    </tr>


  <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-fake-wav2lip.mp4" controls></video>
            <p style="text-align: center;"><b>Fake: </b> Wav2Lip <i>with real voice</i></p>
        </td>
        <td style="width: 50%;">
            <video src="http://data.matsworld.io/ucbresearch/example-fake-wav2lip-ai-voice.mp4" controls ></video> 
            <p style="text-align: center;"><b>Fake: </b> Wav2Lip  <i>with fake voice</i></p>
        </td>
    </tr>
    <tr>
      <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-fake-retalking.mp4" controls></video>
            <p style="text-align: center;"><b>Fake: </b> ReTalking <i>with real voice</i></p>
        </td>
        <td style="width: 50%;">
            <video src="http://data.matsworld.io/ucbresearch/example-fake-retalking-ai-voice.mp4" controls ></video>
            <p style="text-align: center;"><b>Fake: </b> ReTalking <i>with fake voice</i></p>
        </td>
    </tr>
    <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-fake-facefusion.mp4" controls></video>
            <p style="text-align: center;"><b>Fake: </b> Face Fusion</p>
        </td>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-fake-facefusion-gan.mp4" controls ></video>
            <p style="text-align: center;"><b>Fake: </b> Face Fusion + GAN</p>
        </td>
    </tr>
    <tr>
        <td style="width: 50%;">
            <video src="https://data.matsworld.io/ucbresearch/example-fake-facefusion-live.mp4"  style="width: 100%;" controls></video>
            <p style="text-align: center;"><b>Fake: </b> Face Fusion Live</p>
        </td>
        <td style="width: 50%;">
            <p></p>
        </td>
    </tr>
</table>


## Misc

Please use the following citation to refer to our work:

```bib
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.