metadata
license: apache-2.0
language:
- en
size_categories:
- 100M<n<1B
Dataset Card for Dataset Curation of 3DXTalker
Dataset Description
- Repository: [https://github.com/EngineeringAI-LAB/3DXTalker/tree/main]
- Paper: [Comiing Soon]
- Project : [https://engineeringai-lab.github.io/3DXTalker.github.io/]
Dataset Summary
This dataset is a large-scale, curated collection of talking head videos built for tasks such as high-fidelity 3D talking avatar generation, lip synchronization, and pose dynamics modeling.
The dataset aggregates and standardizes data from six prominent sources (GRID, RAVDESS, MEAD, VoxCeleb2, HDTF, Celebv-HQ), processed through a rigorous data curation pipeline to ensure high quality in terms of face alignment, resolution, and audio-visual synchronization. It covers diverse environments (Lab vs. Wild) and a wide range of subjects.
Supported Tasks and Leaderboards
- 3D Talking Head Generation: Synthesizing realistic talking videos from driving speech.
- Audio-Driven Lip Synchronization: Aligning lip movements precisely with input speech.
- Emotion Analysis & Synthesis: Leveraging the emotional diversity in datasets like RAVDESS and MEAD.
- Audio-Driven Head Pose Synthesis: Modeling natural head movements and orientation directly driving speech.
Dataset Structure
trainset/
├── V0-GRID/ # 6,570 sequences from GRID corpus
│ ├── V0-s1-00001/
│ │ ├── audio.wav # (N,) audio data
│ │ ├── cam.npy # (T, 3) camera parameters
│ │ ├── detailcode.npy # (T, 128) facial details
│ │ ├── envelope.npy # (N,) audio envelope
│ │ ├── expcode.npy # (T, 50) expression codes
│ │ ├── lightcode.npy # (T, 9, 3) lighting
│ │ ├── metadata.pkl # Sequence metadata
│ │ ├── posecode.npy # (T, 6) head pose
│ │ ├── refimg.npy # (C, H, W) reference image
│ │ ├── shapecode.npy # (T, 100) shape codes
│ │ └── texcode.npy # (T, 50) texture codes
│ ├── V0-s1-00002/
│ │ └── ... (same 11 files)
│ ├── V0-s1-00003/
│ └── ... (6,570 total sequences)
│
├── V1-RAVDESS/ # 583 sequences from RAVDESS dataset
│ ├── V1-Song-Actor_01-00001/
│ │ └── ... (same 11 files)
│ ├── V1-Song-Actor_01-00002/
│ ├── V1-Speech-Actor_01-00001/
│ ├── V1-Speech-Actor_02-00001/
│ └── ... (583 total sequences)
│
├── V2-MEAD/ # 1,939 sequences from MEAD dataset
│ ├── V2-M003-angry-00001/
│ │ └── ... (same 11 files)
│ ├── V2-M003-angry-00002/
│ ├── V2-M003-happy-00001/
│ ├── V2-W009-sad-00001/
│ └── ... (1,939 total sequences)
│
├── V3-VoxCeleb2/ # 1,296 sequences from VoxCeleb2
│ ├── {sequence_id}/
│ │ └── ... (same 11 files)
│ └── ... (1,296 total sequences)
│
├── V4-HDTF/ # 350 sequences from HDTF dataset
│ ├── {sequence_id}/
│ │ └── ... (same 11 files)
│ └── ... (350 total sequences)
│
└── V5-CelebV-HQ/ # 768 sequences from CelebV-HQ dataset
├── {sequence_id}/
│ └── ... (same 11 files)
└── ... (768 total sequences)
Data Format Details
File Overview
| File | Type | Shape | Description |
|---|---|---|---|
audio.wav |
Audio | (N_samples,) | Original audio waveform |
cam.npy |
Parameters | (N_frames, 3) | Camera parameters (position/scale) |
detailcode.npy |
Parameters | (N_frames, 128) | Facial detail codes (wrinkles, fine features) |
envelope.npy |
Parameters | (N_audio_samples,) | Audio envelope/amplitude over time |
expcode.npy |
Parameters | (N_frames, 50) | FLAME expression parameters (50-dim) |
lightcode.npy |
Parameters | (N_frames, 9, 3) | Spherical harmonics lighting (9 bands × RGB) |
metadata.pkl |
Metadata | N/A | Sequence metadata (integer or dict) |
posecode.npy |
Parameters | (N_frames, 6) | 3 head pose + 3 jaw pose |
refimg.npy |
Image | (3, 224, 224) | Reference image (RGB, 224×224 pixels) |
shapecode.npy |
Parameters | (N_frames, 100) | FLAME shape parameters (100-dim) |
texcode.npy |
Parameters | (N_frames, 50) | Texture codes (50-dim) |
Coordinate Systems and Conventions
- FLAME model: 3D Morphable Face Model with 5023 vertices
- Expression space: 50-dimensional linear basis
- Shape space: 100-dimensional PCA space
- Pose representation: 3 head pose + 3 jaw pose
- Lighting: 2nd-order spherical harmonics (9 bands)
Temporal Synchronization
- Video frames: 25 FPS (frames per second)
- Audio samples: 16,000 samples per second
- All video parameters (
expcode,shapecode,detailcode,posecode,cam,lightcode,texcode) share the sameN_framesdimension - Audio and video are temporally aligned (frame 0 corresponds to start of audio)
Data Statistics
The dataset comprises 11,706 total video samples, spanning approximately 67.4 hours of self-talking footage. The data is categorized by environment (Lab vs. Wild) and includes varying resolutions and subject diversity.
Detailed Statistics (from Curation Pipeline)
| Dataset | ID | Environment | Year | Raw Resolution | Size (samples) | Subject | Total Duration (s) | Hours (h) | Avg. Duration (s/sample) |
|---|---|---|---|---|---|---|---|---|---|
| GRID | V0 | Lab | 2006 | 720 × 576 | 6,600 | 34 | 99,257.81 | 27.57 | 15.04 |
| RAVDESS | V1 | Lab | 2018 | 1280 × 1024 | 613 | 24 | 10,071.88 | 2.80 | 16.43 |
| MEAD | V2 | Lab | 2020 | 1920 × 1080 | 1,969 | 60 | 42,868.77 | 11.91 | 21.77 |
| VoxCeleb2 | V3 | Wild | 2018 | 360P~720P | 1,326 | 1k+ | 21,528.20 | 5.98 | 16.24 |
| HDTF | V4 | Wild | 2021 | 720P~1080P | 400 | 300+ | 55,452.08 | 15.40 | 138.63 |
| Celebv-HQ | V5 | Wild | 2022 | 512 × 512 | 798 | 700+ | 13,486.20 | 3.75 | 16.90 |
Data Splits
The dataset follows a strict training and testing split protocol to ensure fair evaluation. The testing set is composed of a balanced selection from each sub-dataset.
| Dataset | ID | Total Size | Training Set | Test Set |
|---|---|---|---|---|
| GRID | V0 | 6,600 | 6,570 | 30 |
| RAVDESS | V1 | 613 | 583 | 30 |
| MEAD | V2 | 1,969 | 1,939 | 30 |
| VoxCeleb2 | V3 | 1,326 | 1,296 | 30 |
| HDTF | V4 | 400 | 350 | 50 |
| Celebv-HQ | V5 | 798 | 768 | 30 |
| Summary | 11,706 | 11,506 | 200 |
Dataset Creation
Curation Rationale
Raw videos from the wild (e.g., VoxCeleb2, Celebv-HQ) often contain background noise, diverse languages, or varying resolutions. This dataset is the result of the following data curation pipeline designed to ensure high-quality audio-visual consistency:
- Duration Filtering: To facilitate temporal modeling, short clips from lab datasets are concatenated to form 10–20s sequences, while wild samples shorter than 10s are filtered out.
- Signal-to-Noise Ratio (SNR) Filtering: Clips with strong background noise, music, or environmental interference are removed based on SNR thresholds to ensure clean audio features.
- Language Filtering: Linguistic consistency is enforced by using Whisper to discard non-English samples or those with low detection confidence.
- Audio-Visual Sync Filtering: SyncNet is used to eliminate clips with poor lip synchronization, abrupt scene cuts, or off-screen speakers (e.g., voice-overs).
- Resolution Normalization: All videos are resized and center-cropped to a unified 512×512 resolution and re-encoded at 25 FPS with standardized RGB to harmonize data from diverse sources.
Source Video Data
- GRID: https://zenodo.org/records/3625687
- RAVDESS: https://zenodo.org/records/1188976
- MEAD: https://wywu.github.io/projects/MEAD/MEAD.html
- VoxCeleb2: https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html
- HDTF: https://huggingface.co/datasets/global-optima-research/HDTF
- Celebv-HQ: https://github.com/CelebV-HQ/CelebV-HQ/
Citation
If you use this dataset, please cite the original source datasets:
- GRID: Cooke, M., et al. (2006). An audio-visual corpus for speech perception and automatic speech recognition.
- RAVDESS: Livingstone, S. R., & Russo, F. A. (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS).
- MEAD: Wang, K., et al. (2020). MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation.
- VoxCeleb2: Chung, J. S., et al. (2018). VoxCeleb2: Deep Speaker Recognition.
- HDTF: Zhang, Z., et al. (2021). Flow-guided One-shot Talking Face Generation with a High-resolution Audio-visual Dataset.
- CelebV-HQ: Zhu, H., et al. (2022). CelebV-HQ: A Large-Scale Video Facial Attributes Dataset.
And the EMOCA model used for parameter extraction:
- EMOCA: Danecek, R., et al. (2022). EMOCA: Emotion Driven Monocular Face Capture and Animation.
License
Please refer to the original dataset licenses:
- GRID: Research use only
- RAVDESS: CC BY-NA-SC 4.0
- MEAD, VoxCeleb2, HDTF, CelebV-HQ: Check respective dataset licenses
Notes
- Not all sequence numbers are contiguous (some sequences may be missing due to quality filtering or processing failures)
- File counts per sequence are consistent (11 files per sequence)
- This is a processed/derived dataset - original videos are not included, only extracted parameters