ABot World Explorer 500h

ABot World Explorer 500h contains 30,969 action-conditioned video episodes associated with the data infrastructure described in ABot-World-0. Each episode preserves an MP4, dataset-native keyboard actions, captions, and one COLMAP text sparse model.

Dataset facts

The repository name is an identifier, not an audited duration claim. Exact duration, FPS, frame count, and alignment statistics are not claimed by this payload-only publication.

Layout

meta/abot-world-explorer-poster.png
metadata.jsonl
data/<prefix>/<sample_id>/video.mp4
data/<prefix>/<sample_id>/annotations.tar
LICENSE
README.md

sample_id is an anonymous HMAC identifier. annotations.tar is deterministic, uncompressed POSIX USTAR containing action.json, caption.json, and the complete sparse/0/{cameras,images,points3D}.txt COLMAP model. Source keys and OSS locations are not released.

Preview

The Hugging Face Dataset Viewer is backed by metadata.jsonl. It covers all 30,969 episodes and contains only each anonymous sample_id, a typed Video descriptor with an immutable Hub URI for the existing video.mp4, and an immutable, commit-pinned link to the corresponding annotations.tar object. The typed descriptor lets the Dataset Server provide a playable HTTPS media source to the Viewer; it does not copy or rewrite the media payload. It does not copy, rewrite, summarize, or otherwise modify captions, actions, or media payloads.

Data file formats

video.mp4 and annotations.tar

video.mp4 preserves the source MP4 bytes without release-time transcoding. annotations.tar is an uncompressed POSIX USTAR archive. Treat its five members as one sample-level annotation package and validate their paths and hashes before extracting them.

action.json

action.json is a UTF-8 JSON object containing sequence metadata and a frames array. The released records use the following structure; the available controls and dataset-native numeric values may vary by sequence.

Each frames[] object contains frame_id (string), timestamp (number), a keys mapping from control names to booleans, and four length-3 numeric vectors: delta_translation_cam, delta_translation_cam_smooth, delta_euler_deg, and delta_euler_deg_smooth. Rotation deltas are named in degrees; do not infer the translation or timestamp units, thresholds, or exact cross-modal alignment when the released schema metadata marks them as unknown. Some source variants add per-frame translation/rotation threshold and release fields. Readers should accept additional fields and must not assume a fixed set of keys.

caption.json

caption.json is a UTF-8 JSON object with perspective, scene_static, and narrative strings plus a dense_temporal array. dense_temporal may be empty; all current audit canaries contain an empty array, so its item schema is not claimed here. Consumers must not assume at least one segment. String values are preserved except that complete-token private source path identifiers are replaced with [redacted]; all other JSON values are preserved, and JSON keys are never rewritten.

COLMAP pose files

The three files under sparse/0/ form one COLMAP text sparse model and must be interpreted together. Lines beginning with # are comments.

• cameras.txt: one camera per line as CAMERA_ID MODEL WIDTH HEIGHT PARAMS[]. The parameter list depends on the camera model.
• images.txt: two lines per image. The first is IMAGE_ID QW QX QY QZ TX TY TZ CAMERA_ID NAME; the second is a repeated list of (X, Y, POINT3D_ID) observations and may be empty. The pose maps world coordinates to camera coordinates using a Hamilton quaternion. COLMAP camera axes are +X right, +Y down, +Z forward, and the camera center is -R^T T.
• points3D.txt: one sparse point per line as POINT3D_ID X Y Z R G B ERROR TRACK[]; each track item is (IMAGE_ID, POINT2D_IDX). A present but empty file is valid and means that the model contains no sparse 3D points.

IDs are not guaranteed to be contiguous. See the official specifications for cameras.txt, images.txt, and points3D.txt.

Selective download

Use a full commit ID when reproducibility matters. The Preview index provides the anonymous sample_id; the two corresponding payload paths are derived from that ID without consulting source keys or OSS locations.

from huggingface_hub import snapshot_download

REPO_ID = "acvlab/ABot-World-Explorer-500h"
REVISION = "<full Hugging Face commit ID>"

sample_id = "<sample_id from metadata.jsonl>"
prefix = sample_id[:2]
snapshot = snapshot_download(
    repo_id=REPO_ID,
    repo_type="dataset",
    revision=REVISION,
    allow_patterns=[
        "LICENSE",
        "README.md",
        "metadata.jsonl",
        f"data/{prefix}/{sample_id}/video.mp4",
        f"data/{prefix}/{sample_id}/annotations.tar",
    ],
)
print(snapshot)

Before extracting TAR data, require regular files and the exact member allowlist; do not call unchecked extractall().

Intended use and limitations

Intended for research on action-conditioned video prediction, controllable world models, representation learning, and agent learning. It is not a symbolic simulator or a guarantee of physical, causal, demographic, or geographic coverage. Users must assess suitability, bias, safety, and legal obligations for their downstream use.

Additional data access

For additional data access or customized dataset requirements, please contact the AMAP Data Department directly at phys_ai_data@service.alibaba.com

Citation

@misc{jiang2026abotworld0infiniteinteractiveworld,
      title={ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU},
      author={Fan Jiang and Zhaoxu Sun and Mengchao Wang and Ziyu Zhu and Chiyu Wang and Yunpeng Zhang and Wenlin Liu and Yun Wang and Xue Zheng and Rui Sun and Junfeng Ni and Hongyu Pan and Zhongxu Sun and Fei Yu and Zengye Ge and Mengmeng Du and Nianfei Fan and Mingchao Sun and Yu Liu and Yongchang and Yanqing Zhu and Jiahang Wang and Ning Ying and Yuze Xuan and Di Yang and Zhicheng Liu and Zhe Gao and Tingbing Xu and Jiacheng Sui and Wenjin Yang and Junnan Lai and Shufeng Liu and Yuan Liu and Zheng Zhou and Yingliang Peng and Dawei Cao and Kaifeng Sheng and Yuxiang Cai and Fei Lu and Mu Xu and Ning Guo},
      year={2026},
      eprint={2607.19191},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.19191},
}