README.md

metadata

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

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.

Due to policy restrictions, this public release contains the policy-compliant subset of the collected real-world data. At the released sampling rate of 4 fps, the open-sourced subset contains about 100 hours of navigation data.

Subset	Episodes	RGB frames	Duration @ 4 fps	Task
bvn	5,417	695,373	48.29 h	Beyond-the-View Navigation
ifn	5,625	712,290	49.46 h	Instruction-Following Navigation
Total	11,042	1,407,663	97.75 h	-

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
        └── ...

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 public release uses main.
episode_id	string	Unique episode identifier.
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	Original relative episode image directory before sharding.
rgb_dir	string	Original relative RGB frame directory.
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.
annotation_file	string	Annotation file path in this repository.
frame_files	list[string]	RGB frame paths inside the shard after extraction.
asset_dirs	list[string]	Original episode asset directories included in the shard.
shard	string	Tar shard containing the episode frames.

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": "36e980e5a1ff4817",
  "instruction": "please go along with the rail until you are near by a red cone.",
  "num_frames": 177,
  "num_actions": 177,
  "rgb_dir": "images/0e0ec1bdcc61a4ef/rgb",
  "frame_files": ["images/0e0ec1bdcc61a4ef/rgb/001.jpg"],
  "shard": "ifn/shards/ifn-00000.tar.zst"
}

annotations.json stores the original 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, frame count, and file 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, frame_files resolve to paths such as:

images/<episode_dir>/rgb/001.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}
}