Update README.md

README.md

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 - en
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 - 100M<n<1B
-task_categories:
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----
+---
+
+
+# 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      | 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:
+
+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:** 
+- **RAVDESS:** 
+- **MEAD:** 
+- **VoxCeleb2:** 
+- **HDTF:** 
+- **Celebv-HQ:** 
+