README.md

---
viewer: false
---

# OmniHuman Dataset

OmniHuman is a large-scale, human-centric audio-visual dataset for video
understanding and generation. Each sample provides a multimodal annotation
(`sample_json`): id-aware video captions, structured subject descriptions, speech
transcripts (with timing, language, and emotion), audio captions, and basic video
attributes (fps, duration, resolution). Per-frame body/hand tracking
(`tracking_npz`) is also included.

Original videos are **not** redistributed. Instead, every sample includes its
YouTube `source_url` together with `clip_start_sec` / `clip_end_sec`, so you can
locate and download the exact source clip yourself.

## What's included

All assets are stored as tar shards under `archives/`, each with an index CSV:

| Asset | Archive | Description |
| ----- | ------- | ----------- |
| `sample_json`  | `sample_json_part_*.tar.gz` + `sample_json_index.csv` | Per-sample audio-visual annotation: captions, subjects, speech, audio |
| `metadata`     | `metadata_part_*.tar.gz` + `metadata_index.csv` | JSONL index files for scanning and loading samples |
| `tracking_npz` | `tracking_npz_part_*.tar` + `tracking_npz_index.csv` | Per-frame SMPL/MANO body & hand tracking (`.npz`) |

```text
omnihuman/
├── README.md
├── scripts/        # extraction & utility scripts
└── archives/
    ├── sample_json_index.csv
    ├── sample_json_part_*.tar.gz
    ├── metadata_index.csv
    ├── metadata_part_*.tar.gz
    ├── tracking_npz_index.csv
    └── tracking_npz_part_*.tar
```

The `train/` and `test/` directories are reconstructed by extracting the archives.

## `sample_json` content

`sample_json/xxx.json` is the per-sample audio-visual annotation. It typically
contains:

1. **Source** — `source_url` (YouTube link), `clip_start_sec`, `clip_end_sec`.
2. **Captions** — English/Chinese captions, REF-linked variants (double-person case),
   replacement-text variants, and audio captions.
3. **Speech** — speaker language, transcript text, emotion, on/offscreen flags.
4. **Subjects** (`output`) — appearance, action, expression, position, subject type,
   main-subject flag.
5. **Audio / video metadata** — background audio fields, `fps`, duration, resolution.
6. **Person & identity** — `person_id`, matched identity (e.g. `REF_1`), `face_id`,
   frame span, audio alignment, blur/quality statistics.
7. **Quality/consistency signals** — e.g. `semantic_consistency`.

## Download from Hugging Face

You need the full `archives/` contents on disk before extraction.

### Option A: `git lfs`

```bash
git lfs install
git clone https://huggingface.co/datasets/<HF_DATASET_ID>
cd <HF_DATASET_ID>
git lfs pull
```

### Option B: `huggingface-cli`

```bash
pip install -U "huggingface_hub[cli]"
huggingface-cli login
huggingface-cli download <HF_DATASET_ID> --repo-type dataset --local-dir . --local-dir-use-symlinks False
```

## Extract from archives

Run from the **repo root** (the directory containing `archives/` and `scripts/`):

```bash
for asset in sample_json metadata tracking_npz; do
  python scripts/extract_asset_from_archives.py --repo-root . --asset "$asset" --all
done
```

Add `--skip-existing` to resume after an interruption.

Shards can also be unpacked with plain `tar` (member paths match the `relpath`
column of the index):

```bash
tar xzf archives/sample_json_part_00000.tar.gz
tar xf  archives/tracking_npz_part_00000.tar
```

## Dataset layout (after extraction)

The repo root contains `train/` and `test/` splits, each divided into `single/`
(single-person) and `double/` (double-person) subsets:

```text
omnihuman/
├── archives/
├── train/
│   ├── single/
│   │   ├── sample_json/
│   │   ├── metadata/
│   │   └── tracking_npz/
│   └── double/
│       └── ...
└── test/
    └── ...
```

| Folder          | Description                                       |
| --------------- | ------------------------------------------------- |
| `sample_json/`  | Per-sample audio-visual annotation (captions, subjects, speech, audio) |
| `metadata/`     | JSONL index files for scanning and loading        |
| `tracking_npz/` | Tracking `.npz`. For `double/` samples both persons are in the same file. |

### Naming and sharding

- Sample name is derived from the original clip stem.
- Duplicate basenames are disambiguated with `__dupXXXX`.
- Metadata files are named `<split>_<subset>-NNNNN.jsonl` (e.g. `train_single-00000.jsonl`).
- Each shard contains up to 2000 samples.

## Index CSV format

Each `archives/<asset>_index.csv` has columns:

```text
relpath,archive,member,size_bytes
```

- `relpath`: repo-relative path restored on extraction.
- `archive`: repo-relative tar shard path.
- `member`: tar member path (same as `relpath`).
- `size_bytes`: original (uncompressed) file size.