README.md

---
license: cc-by-nc-sa-4.0
language:
- en
pretty_name: SparseVideoNav Datasets
task_categories:
- robotics
- visual-question-answering
tags:
- embodied-ai
- vision-language-navigation
- robot-navigation
- video
- trajectory
- sparsevideonav
- opendrivelab
configs:
- config_name: bvn
  data_files:
  - split: train
    path: bvn/data.jsonl
- config_name: ifn
  data_files:
  - split: train
    path: ifn/data.jsonl
---

# SparseVideoNav Datasets

![SparseVideoNav dataset overview](assets/dataset_mosaic.png)

This repository contains the real-world navigation datasets released with [OpenDriveLab/SparseVideoNav](https://github.com/OpenDriveLab/SparseVideoNav):

- **BVN**: Beyond-the-View Navigation.
- **IFN**: Instruction-Following Navigation.

Project links:

- Project page: https://opendrivelab.com/SparseVideoNav
- GitHub: https://github.com/OpenDriveLab/SparseVideoNav
- Paper: https://arxiv.org/abs/2602.05827

## Dataset Summary

SparseVideoNav studies real-world vision-language navigation with sparse future video generation. The datasets contain language instructions, RGB frame sequences, and low-level navigation actions. The number of actions matches the number of RGB frames for every released episode.

This repository version contains the processed IFN and BVN subsets used by SparseVideoNav. The complete dataset contains about 140 hours; due to regional policy restrictions, the currently open-sourced portion is approximately 121.74 hours.

| Subset | Episodes | RGB frames | Duration @ 4 fps | Task |
| --- | ---: | ---: | ---: | --- |
| `bvn` | 5,433 | 825,786 | 57.35 h | Beyond-the-View Navigation |
| `ifn` | 6,260 | 927,268 | 64.39 h | Instruction-Following Navigation |
| **Total** | **11,693** | **1,753,054** | **121.74 h** | - |

Duration is computed as `num_frames / 4 / 3600`.

## Repository Structure

Images are stored in compressed tar shards to avoid hundreds of thousands of small files in the Hugging Face repository. Each shard preserves the original relative paths.

```text
.
├── README.md
├── assets/
│   └── dataset_mosaic.png
├── bvn/
│   ├── annotations.json
│   ├── data.jsonl
│   ├── merge_info.json
│   ├── shard_manifest.jsonl
│   └── shards/
│       ├── bvn-00000.tar.zst
│       └── ...
└── ifn/
    ├── annotations.json
    ├── data.jsonl
    ├── merge_info.json
    ├── shard_manifest.jsonl
    └── shards/
        ├── ifn-00000.tar.zst
        └── ...
```

Current shard counts:

| Subset | Shards | Compressed shard bytes |
| --- | ---: | ---: |
| `bvn` | 8 | 14,597,684,355 |
| `ifn` | 9 | 16,623,840,035 |

## Data Format

Each line in `bvn/data.jsonl` or `ifn/data.jsonl` is an episode-level JSON object.

| Field | Type | Description |
| --- | --- | --- |
| `dataset` | string | Dataset subset name, either `bvn` or `ifn`. |
| `subset` | string | Release subset marker. The current release uses `main`. |
| `episode_id` | string | Unique episode identifier. This matches the `id` field in `annotations.json`. |
| `instruction` | string | Primary natural-language navigation instruction. |
| `instructions` | list[string] | Instruction list. Current records contain one instruction. |
| `task_type` | string | Task label, e.g. `beyond_the_view_navigation` or `instruction_following_navigation`. |
| `split` | string | Dataset split. Current release uses `train`. |
| `image_dir` | string | Relative episode image directory after extraction. |
| `rgb_dir` | string | Relative RGB frame directory after extraction. |
| `num_frames` | integer | Number of RGB frames in the episode. |
| `num_actions` | integer | Number of low-level actions. This matches `num_frames`. |
| `actions` | list[object] | Per-frame low-level navigation actions. Each action has `dx`, `dy`, and `dyaw`. |

Each action object contains:

| Field | Type | Description |
| --- | --- | --- |
| `dx` | float | Relative forward/backward displacement for the corresponding step. |
| `dy` | float | Relative lateral displacement for the corresponding step. |
| `dyaw` | float | Relative yaw change for the corresponding step. |

Example:

```json
{
  "dataset": "ifn",
  "episode_id": "<episode_id>",
  "instruction": "please go along with the rail until you are near by a red cone.",
  "num_frames": 177,
  "num_actions": 177,
  "rgb_dir": "images/<episode_dir>/rgb",
  "actions": [{"dx": 0.0429, "dy": -0.0311, "dyaw": 0.0271}]
}
```

`annotations.json` stores the annotation records with the core fields `id`, `video`, `actions`, and `instructions`. `shard_manifest.jsonl` stores shard-level metadata, including the shard path, episode ids, raw byte size, compressed byte size, and frame count.

## Usage

Load episode metadata with Hugging Face Datasets:

```python
from datasets import load_dataset

bvn = load_dataset("OpenDriveLab/SparseVideoNav", "bvn")
ifn = load_dataset("OpenDriveLab/SparseVideoNav", "ifn")
```

Download and inspect shards:

```bash
tar -I zstd -tf ifn/shards/ifn-00000.tar.zst | head
tar -I zstd -xf ifn/shards/ifn-00000.tar.zst
```

After extraction, image paths resolve to paths such as:

```text
images/<episode_dir>/rgb/000.jpg
```

## License

The dataset is released under CC BY-NC-SA 4.0.

## Citation

```bibtex
@article{zhang2026sparse,
  title={Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation},
  author={Zhang, Hai and Liang, Siqi and Chen, Li and Li, Yuxian and Xu, Yukuan and Zhong, Yichao and Zhang, Fu and Li, Hongyang},
  journal={arXiv preprint arXiv:2602.05827},
  year={2026}
}
```