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3DTalkingDataset

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The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Dataset Card for Dataset Curation of 3DXTalker

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 same N_frames dimension
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

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