---
title: TalkingHeadBench
canonical_url: "https://www.modelscope.cn/datasets/axxiong/TalkingHeadBench"
md_url: "https://www.modelscope.cn/datasets/axxiong/TalkingHeadBench.md"
repository: axxiong/TalkingHeadBench
last_updated: 2026-06-27
license: "MIT License"
storage_size: "11 GB"
domain:
  - video
tasks:
  - video-generation
downloads: 46750
stars: 0
---

# TalkingHeadBench

> TalkingHeadBench - axxiong 在 ModelScope 开源的数据集。Project page | Paper

axxiong/TalkingHeadBench 是 ModelScope 魔搭社区上的video-generation数据集，涉及 video 领域，存储大小 11 GB，采用 MIT License 许可。

- **Repository**: axxiong/TalkingHeadBench
- **License**: MIT License
- **Tasks**: video-generation
- **Domain**: video
- **Storage size**: 11 GB
- **Downloads**: 46750
- **Stars**: 0
- **Last updated**: 2026-06-27

Source: https://www.modelscope.cn/datasets/axxiong/TalkingHeadBench

---

# TalkingHeadBench

[Project page](https://anaxqx.github.io/talkingheadbench.github.io) | [Paper](https://huggingface.co/papers/2505.24866)

## Overview

The **TalkingHeadBench(THB)** is a curated dataset designed to support the training and evaluation of deepfake detection models, especially in audio-visual and cross-method generalization scenarios. It includes synthetic videos generated using six modern face animation techniques:

- **LivePortrait**
- **AniPortraitAudio**
- **AniPortraitVideo**
- **Hallo**
- **Hallo2**
- **EmoPortrait**

Each video is named using the format:

    [image]--[driving_signals]--[generation_method].mp4

- `image`: identity image from **FFHQ**
- `driving_signals`: facial motion and optionally audio from **CelebV-HQ**
- `generation_method`: the name of the generator used

## Dataset Structure

```
TalkingHeadBench/
├── fake/
│   ├── [generator_name]/[split]/*.mp4
│   ├── additional_dataset/[generator_name]/*.mp4  # Additional evaluation-only dataset generated using MAGI-1 and Hallo3.
├── audio/
│   ├── fake/*.wav         # From CelebV-HQ driving signals
│   ├── ff++/*.wav         # From FaceForensics++ YouTube videos
├── true/
│   ├── true_dataset_split.json         # Filenames of true (non-deepfake) videos collected from CelebV-HQ and FaceForensics++
└── README.md
```

- Each generator has three splits: `train`, `val`, and `test`
- Training and testing sets come from **disjoint identity pools**
- ~300 fake videos per generator are used for training
- 50 videos per generator are held out as validation
- Testing uses entirely unseen identities

## True Dataset
- For training and evaluating purposes, we added true (non-deepfake) videos to the process at an approximately 1:1 ratio
- We used CelebV-HQ and FaceForensics++ (`faceforensics++/original_sequences/youtube/raw/videos`) for our source of true videos
- All the true videos are checked against both driving signals and images to ensure no id leakage.

## Audio Details

- **Fake Audio** (`audio/fake/`): Extracted from the CelebV-HQ videos that drove the generation.
- **True Audio** (`audio/ff++/`): Original audio from the FaceForensics++ YouTube videos, named as `xxx.wav` (three-digit index).
  - Maps directly to: `faceforensics++/original_sequences/youtube/raw/videos`
  - We provide 704 audio clips out of 1000, due to public unavailability of some videos.

## Applications

- Audio-visual deepfake detection
- Modality-specific detection (audio-only or video-only)
- Cross-generator generalization testing
- Audio-video consistency evaluation

## Licensing and Attribution

Please ensure compliance with the original licenses:

- [FFHQ License](https://github.com/NVlabs/ffhq-dataset#license)
- [CelebV-HQ License](https://github.com/CelebV-HQ/CelebV-HQ#license)
- [FaceForensics++ License](https://github.com/ondyari/FaceForensics#license)

## Citation
 
If you use this dataset in your research, please cite the relevant original sources (FFHQ, CelebV-HQ, FaceForensics++) and the associated paper
```
@article{xiong2025talkingheadbench,
  title={TalkingHeadBench: A Multi-Modal Benchmark \& Analysis of Talking-Head DeepFake Detection},
  author={Xiong, Xinqi and Patel, Prakrut and Fan, Qingyuan and Wadhwa, Amisha and Selvam, Sarathy and Guo, Xiao and Qi, Luchao and Liu, Xiaoming and Sengupta, Roni},
  journal={arXiv preprint arXiv:2505.24866},
  year={2025}
}
```