Update README.md
Browse files# Dataset Card for Dataset Curation of 3DXTalker
## Dataset Description
- **Repository:** [Link to your GitHub/Project Page]
- **Paper:** [Link to your 3DXTalker or relevant paper]
- **Project :** [Link to your Project Page]
### 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 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 | 1296 | 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:
1. **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.
2. **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.
3. **Language Filtering:** Linguistic consistency is enforced by using **Whisper** to discard non-English samples or those with low detection confidence.
4. **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).
5. **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:** High-quality sentence-level corpus.
- **RAVDESS:** Emotional speech and song.
- **MEAD:** Multi-view emotional audio-visual dataset.
- **VoxCeleb2:** Large-scale speaker identification dataset (Wild).
- **HDTF:** High-definition talking face dataset (Wild).
- **Celebv-HQ:** High-quality celebrity video dataset (Wild).