SparseVideoNav Datasets

This repository contains the real-world navigation datasets released with 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.

.
├── 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:

{
  "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:

from datasets import load_dataset

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

Download and inspect shards:

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:

images/<episode_dir>/rgb/000.jpg

License

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

Citation

@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}
}