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README.md

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+---
+license: cc-by-nc-sa-4.0
+size_categories:
+- n>1T
+task_categories:
+- text-to-video
+- image-to-video
+- image-to-3d
+- robotics
+- other
+language:
+- en
+pretty_name: OmniWorld
+---
+
+<h1 align='center'>OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling</h1>
+<div align='center'>
+    <a href='https://github.com/yangzhou24' target='_blank'>Yang Zhou</a><sup>1</sup> 
+    <a href='https://github.com/yyfz' target='_blank'>Yifan Wang</a><sup>1</sup> 
+    <a href='https://zhoutimemachine.github.io' target='_blank'>Jianjun Zhou</a><sup>1,2</sup> 
+    <a href='https://github.com/AmberHeart' target='_blank'>Wenzheng Chang</a><sup>1</sup> 
+    <a href='https://github.com/ghy0324' target='_blank'>Haoyu Guo</a><sup>1</sup> 
+    <a href='https://github.com/LiZizun' target='_blank'>Zizun Li</a><sup>1</sup> 
+    <a href='https://kaijing.space/' target='_blank'>Kaijing Ma</a><sup>1</sup> 
+    
+</div>
+<div align='center'>
+<a href='https://scholar.google.com/citations?user=VuTRUg8AAAAJ' target='_blank'>Xinyue Li</a><sup>1</sup> 
+    <a href='https://scholar.google.com/citations?user=5SuBWh0AAAAJ&hl=en' target='_blank'>Yating Wang</a><sup>1</sup> 
+    <a href='https://www.haoyizhu.site/' target='_blank'>Haoyi Zhu</a><sup>1</sup> 
+    <a href='https://mingyulau.github.io/' target='_blank'>Mingyu Liu</a><sup>1,2</sup> 
+    <a href='https://scholar.google.com/citations?user=FbSpETgAAAAJ' target='_blank'>Dingning Liu</a><sup>1</sup> 
+    <a href='https://yangjiangeyjg.github.io/' target='_blank'>Jiange Yang</a><sup>1</sup> 
+    <a href='https://github.com/Kr1sJFU' target='_blank'>Zhoujie Fu</a><sup>1</sup> 
+    
+</div>
+<div align='center'>
+    <a href='https://sotamak1r.github.io/' target='_blank'>Junyi Chen</a><sup>1</sup> 
+    <a href='https://cshen.github.io' target='_blank'>Chunhua Shen</a><sup>1,2</sup> 
+    <a href='https://oceanpang.github.io' target='_blank'>Jiangmiao Pang</a><sup>1</sup> 
+    <a href='https://kpzhang93.github.io/' target='_blank'>Kaipeng Zhang</a><sup>1</sup>
+    <a href='https://tonghe90.github.io/' target='_blank'>Tong He</a><sup>1†</sup>
+</div>
+<div align='center'>
+    <sup>1</sup>Shanghai AI Lab  <sup>2</sup>ZJU 
+</div>
+<br>
+<div align="center">
+  <a href="https://yangzhou24.github.io/OmniWorld/"><img src="https://img.shields.io/badge/Project Page-5745BB?logo=google-chrome&logoColor=white"></a>  
+  <a href="https://arxiv.org/abs/2509.12201"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv&color=red&logo=arxiv"></a>  
+  <a href="https://github.com/yangzhou24/OmniWorld"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a>  
+  <a href="https://huggingface.co/datasets/InternRobotics/OmniWorld"><img src="https://img.shields.io/static/v1?label=Dataset&message=HuggingFace&color=yellow&logo=huggingface"></a>  
+</div>
+
+
+# 🎉NEWS
+- [2025.9.16] 🔥 The first **1.2k splits** of **OmniWorld-Game** is now live on Hugging Face! **We will continue to update, more data is coming soon,  Stay tuned!**
+
+# Directory Structure
+```
+DATA_PATH/
+├─ annotations/
+│  ├─ OmniWorld-Game/
+│  │  ├─ b04f88d1f85a/
+│  │  ├─ 52e80f590716/
+│  │  └─ …                   # one folder per scene
+│  ├─ OmniWorld-DROID/
+│  └─ …
+├─ metadata/
+│  ├─ OmniWorld-Game_metadata.csv
+│  ├─ OmniWorld-DROID_metadata.csv
+│  └─ …
+├─ videos/
+│  ├─ OmniWorld-Game/
+│  │  ├─ b04f88d1f85a/
+│  │  ├─ 52e80f590716/
+│  │  └─ …
+│  ├─ OmniWorld-DROID/
+│  └─ …
+└─ README.md                # this guide
+```
+
+
+# Dataset Download
+You can download the entire OmniWorld dataset using the following command:
+```bash
+# 1. Install (if you haven't yet)
+pip install --upgrade "huggingface_hub[cli]"
+
+# 2. Full download
+hf download InternRobotics/OmniWorld \
+           --repo-type dataset \
+           --local-dir /path/to/DATA_PATH
+```
+For downloading specific files (instead of the full dataset), please refer to the [dowanload_specific.py](https://github.com/yangzhou24/OmniWorld/blob/main/scripts/dowanload_specific.py) provided in our GitHub repository.
+
+## OmniWorld-Game Organisation
+
+To keep the download manageable, each scene is split into multiple `.tar.gz` files:
+
+- RGB / Depth / Flow : ≤ 2 000 images per `.tar.gz`. The naming convention follows the format: `…/<scene_id>_<modality>_<part_idx>.tar.gz`
+
+- Other Annotations: Additional data such as camera poses, masks, and text annotations are grouped together in a single file per scene: `…/<scene_id>_others.tar.gz`
+
+# Usage Guide
+## OmniWorld-Game
+### 1. Quick-Start: Extracting One Scene
+Below we extract RGB frames and all annotations for scene `<scene_id>` to a local folder of the same name.
+```bash
+scene_id=b04f88d1f85a
+root=/path/to/DATA_PATH        # where you store OmniWorld
+
+mkdir -p ${scene_id}
+
+# --- RGB (may span several parts) ------------------------------------------
+for rgb_tar in ${root}/videos/OmniWorld-Game/${scene_id}/${scene_id}_rgb_*.tar.gz
+do
+    echo "Extracting $(basename $rgb_tar)…"
+    tar -xzf "$rgb_tar" -C ${scene_id}
+done
+
+# --- Depth -----------------------------------------------------------------
+for d_tar in ${root}/annotations/OmniWorld-Game/${scene_id}/${scene_id}_depth_*.tar.gz
+do
+    echo "Extracting $(basename $d_tar)…"
+    tar -xzf "$d_tar" -C ${scene_id}
+done
+
+# --- Flow ------------------------------------------------------------------
+for f_tar in ${root}/annotations/OmniWorld-Game/${scene_id}/${scene_id}_flow_*.tar.gz
+do
+    echo "Extracting $(basename $f_tar)…"
+    tar -xzf "$f_tar" -C ${scene_id}
+done
+
+# --- All other annotations --------------------------------------
+tar -xzf ${root}/annotations/OmniWorld-Game/${scene_id}/${scene_id}_others.tar.gz -C ${scene_id}
+```
+Resulting Scene Folder: 
+```
+b04f88d1f85a/
+├─ color/              # RGB frames (.png)
+├─ depth/              # 16-bit depth maps
+├─ flow/               # flow_u_16.png / flow_v_16.png / flow_vis.png
+├─ camera/             # split_*.json (intrinsics + extrinsics)
+├─ subject_masks/      # foreground masks (per split)
+├─ gdino_mask/         # dynamic-object masks (per frame)
+├─ text/               # structured captions (81-frame segments)
+├─ droidclib/          # coarse camera poses (if you need them)
+├─ fps.txt             # source video framerate
+└─ split_info.json     # how frames are grouped into splits
+```
+
+### 2. Detailed Explanation
+
+#### 1. Split Information (`split_info.json`)
+
+Each scene is divided into several high-quality "splits". `split_info.json` tells you how the original video indices are grouped.
+
+```
+{
+  "scene_name": "b04f88d1f85a",
+  "split_num": 6,
+  "split": [
+    [0, 1, 2, ...],          // split_0
+    [316, 317, ...],         // split_1
+    ...
+  ]
+}
+```
+Meaning:
+
+- `split_num` – total number of splits in this scene.
+- `split[i]` – an array with the original frame indices belonging to `split i`.
+#### 2. Camera Poses (`camera/split_<idx>.json`)
+
+For every split you will find a file
+```
+<scene_name>/camera/split_<idx>.json   (e.g. split_0.json)
+```
+containing:
+- `focals` – focal length in pixels (same for x and y).
+- `cx, cy` – principal point.
+- `quats` – per-frame rotation as quaternions (w, x, y, z).
+- `trans` – per-frame translation (x, y, z).
+
+**Minimal Reader**
+
+```python
+import json
+from pathlib import Path
+
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+
+
+def load_split_info(scene_dir: Path):
+    """Return the split json dict."""
+    with open(scene_dir / "split_info.json", "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def load_camera_poses(scene_dir: Path, split_idx: int):
+    """
+    Returns
+    -------
+    intrinsics : (S, 3, 3) array, pixel-space K matrices
+    extrinsics : (S, 4, 4) array, OpenCV world-to-camera matrices
+    """
+    # ----- read metadata -----------------------------------------------------
+    split_info = load_split_info(scene_dir)
+    frame_count = len(split_info["split"][split_idx])
+
+    cam_file = scene_dir / "camera" / f"split_{split_idx}.json"
+    with open(cam_file, "r", encoding="utf-8") as f:
+        cam = json.load(f)
+
+    # ----- intrinsics --------------------------------------------------------
+    intrinsics = np.repeat(np.eye(3)[None, ...], frame_count, axis=0)
+    intrinsics[:, 0, 0] = cam["focals"]          # fx
+    intrinsics[:, 1, 1] = cam["focals"]          # fy
+    intrinsics[:, 0, 2] = cam["cx"]              # cx
+    intrinsics[:, 1, 2] = cam["cy"]              # cy
+
+    # ----- extrinsics --------------------------------------------------------
+    extrinsics = np.repeat(np.eye(4)[None, ...], frame_count, axis=0)
+
+    # SciPy expects quaternions as (x, y, z, w) → convert
+    quat_wxyz = np.array(cam["quats"])           # (S, 4)  (w,x,y,z)
+    quat_xyzw = np.concatenate([quat_wxyz[:, 1:], quat_wxyz[:, :1]], axis=1)
+
+    rotations = R.from_quat(quat_xyzw).as_matrix()      # (S, 3, 3)
+    translations = np.array(cam["trans"])               # (S, 3)
+
+    extrinsics[:, :3, :3] = rotations
+    extrinsics[:, :3, 3] = translations
+
+    return intrinsics.astype(np.float32), extrinsics.astype(np.float32)
+
+
+# --------------------------- example usage -----------------------------------
+if __name__ == "__main__":
+    scene = Path("b04f88d1f85a")   # adjust to your path
+    K, w2c = load_camera_poses(scene, split_idx=0)      # world-to-camera transform in OpenCV format
+    print("Intrinsics shape:", K.shape)
+    print("Extrinsics shape:", w2c.shape)
+```
+
+#### 3. Depth (`depth/<frame_idx>.png`)
+
+- 16-bit PNG, one file per RGB frame.
+- Values are stored as unsigned integers in [0, 65535].
+
+   &ensp;&ensp;&ensp;`0 … 100`  ≈ invalid / too close
+
+   &ensp;&ensp;&ensp;`65500 … 65535` ≈ sky / too far
+
+**Minimal Reader**
+
+```python
+import imageio.v2 as iio
+import numpy as np
+from pathlib import Path
+
+
+def load_depth(depthpath):
+    """
+    Returns
+    -------
+    depthmap : (H, W) float32
+    valid   : (H, W) bool      True for reliable pixels
+    """
+
+    depthmap = imageio.v2.imread(depthpath).astype(np.float32) / 65535.0
+    near_mask = depthmap < 0.0015   # 1. too close
+    far_mask = depthmap > (65500.0 / 65535.0) # 2. filter sky
+    # far_mask = depthmap > np.percentile(depthmap[~far_mask], 95) # 3. filter far area (optional)
+    near, far = 1., 1000.
+    depthmap = depthmap / (far - depthmap * (far - near)) / 0.004
+
+    valid = ~(near_mask | far_mask)
+    depthmap[~valid] = -1
+
+    return depthmap, valid
+
+# ---------------------------- example ---------------------------------------
+if __name__ == "__main__":
+    d, mask = load_depth("b04f88d1f85a/depth/000000.png")
+    print("Depth shape:", d.shape, "valid pixels:", mask.mean() * 100, "%")
+
+```
+Feel free to tighten the `far_mask` with `np.percentile(depthmap[~far_mask], 95)` if you need a stricter “too-far” criterion.
+
+> We provide a script to generate a fused point cloud from camera poses and depth maps. Instructions can be found in the [Point Cloud Visualization](https://github.com/yangzhou24/OmniWorld?tab=readme-ov-file#-visualize-as-point-cloud) section from our github repository.
+
+#### 4. Structured Caption (`text/<start_idx>_<end_idx>.json`)
+
+From every split we sample `81` frames and attach rich, structured captions.
+
+The general naming format of the text file is `<start_idx>_<end_idx>.json`, which means that the text is the description of the `start_idx` frame to the `end_idx` frame of the global video.
+
+Each text file contains the following description information
+- `Short_Caption`: A brief summary (1–2 sentences).
+- `PC_Caption`: Actions and status of the player-character.
+- `Background_Caption`: Fine-grained spatial description of the scene.
+- `Camera_Caption`: How the camera moves, such as zooms, rotates.
+- `Video_Caption`: ≈200-word dense paragraph combining all above..
+- `Key_Tags`: string of tags that combines key features.
+
+#### 5. Foreground Masks (`subject_masks/split_<idx>.json`)
+Binary masks (white = subject, black = background) for every frame in a split. Main masked object includes:
+
+- `Human/Robotics` scenes: the active arm / robot.
+- `Game` scenes: the playable character or vehicle.
+
+**Minimal Reader**
+```python
+import json
+from pathlib import Path
+from pycocotools import mask as mask_utils
+import numpy as np
+
+def load_subject_masks(scene_dir: Path, split_idx: int):
+    """
+    Returns
+    -------
+    masks : list[np.ndarray]  (H, W) bool
+    """
+    seg_mask_list = []
+    segmask_path = scene_dir / "subject_masks" / f"split_{split_idx}.json"
+    with open(segmask_path, "r", encoding="utf-8") as f:
+        seg_masks = json.load(f)
+    for key in seg_masks.keys():
+        seg_mask = seg_masks[key]
+        seg_mask = mask_utils.decode(seg_mask["mask_rle"])
+        seg_mask_list.append(seg_mask)
+
+    seg_mask_list
+
+# ---------------------------- example ---------------------------------------
+if __name__ == "__main__":
+    masks = load_subject_masks(Path("b04f88d1f85a"), split_idx=0)
+    print("Loaded", len(masks), "masks of shape", masks[0].shape)
+```
+We also release per-frame Dynamic Masks (`gdino_mask/<frame_idx>.png`). Each RGB image in the original video is labeled with dynamic objects (such as cars, people, and animals). White represents dynamic objects, and black represents static backgrounds. This can be used in conjunction with Foreground Masks as needed.
+
+
+#### 6. Optical Flow (`flow/<frame_idx>/...`)
+
+For every RGB frame `t` we provide dense forward optical flow that points to frame `t + 1`.
+
+Directory layout (example for frame 0 of scene `b04f88d1f85a`)
+```
+b04f88d1f85a/
+└─ flow/
+   └─ 00000/
+      ├─ flow_u_16.png   # horizontal component  (u, Δx)
+      ├─ flow_v_16.png   # vertical component    (v, Δy)
+      └─ flow_vis.png    # ready-made RGB visualisation (for inspection only)
+```
+**Minimal Reader**
+```python
+import numpy as np
+import imageio.v2 as iio
+from pathlib import Path
+
+FLOW_MIN, FLOW_MAX = -300.0, 300.0           # change if you override the range
+
+def flow_decompress(u, v, flow_min=-FLOW_MIN, flow_max=FLOW_MAX):
+    """
+    Read uint16 image and convert back to optical flow data
+
+    Args:
+        u: np.array (np.uint16) - Optical flow horizontal component
+        v: np.array (np.uint16) - Optical flow vertical component
+        flow_min: float - Assumed minimum value of optical flow
+        flow_max: float - Assumed maximum value of optical flow
+
+    Returns:
+        np.array (np.float32) - Optical flow data with shape (H,W,2)
+    """
+    u = u.astype(np.uint16)
+    v = v.astype(np.uint16)
+
+    u = u / 65535.0
+    v = v / 65535.0
+
+    u = u * (flow_max - flow_min) + flow_min
+    v = v * (flow_max - flow_min) + flow_min
+
+    res = np.stack((u, v), axis=-1)
+
+    return res.astype(np.float32)
+
+def load_flow(flowpath):
+    of_u_path = os.path.join(flowpath, "flow_u_16.png")
+    of_v_path = os.path.join(flowpath, "flow_v_16.png")
+
+    u = cv2.imread(str(of_u_path)).astype(np.uint16)
+    v = cv2.imread(str(of_v_path)).astype(np.uint16)
+    flow = flow_decompress(u, v)
+
+    return flow
+
+# ---------------------------- example ---------------------------------------
+if __name__ == "__main__":
+    flow = load_flow("b04f88d1f85a/flow/00000")
+    print("Flow shape: ", flow.shape)
+```
+
+
+# License
+The OmniWorld dataset is released under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)**. By accessing or using this dataset, you agree to be bound by the terms and conditions outlined in this license, as well as the specific provisions detailed below.
+
+- **Special Note on Third-Party Content**:
+A portion of this dataset is derived from third-party game content. All intellectual property rights pertaining to these original game assets (including, but not limited to, RGB and depth images) remain with their respective original game developers and publishers.
+
+- **Permitted Uses**:
+You are hereby granted permission, free of charge, to use, reproduce, and share the OmniWorld dataset and any adaptations thereof, solely for non-commercial research and educational purposes. This includes, but is not limited to: academic publications, algorithm benchmarking, reproduction of scientific results.
+
+Under this license, you are expressly **forbidden** from:
+
+- Using the dataset, in whole or in part, for any commercial purpose, including but not limited to its incorporation into commercial products, services, or monetized applications.
+
+- Redistributing the original third-party game assets contained within the dataset outside the scope of legitimate research sharing.
+Removing or altering any copyright, license, or attribution notices.
+
+The authors of the OmniWorld dataset provide this dataset "as is" and make no representations or warranties regarding the legality of the underlying data for any specific purpose. Users are solely responsible for ensuring that their use of the dataset complies with all applicable laws and the terms of service or license agreements of the original game publishers (sources of third-party content).
+
+For the full legal text of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, please visit: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.
+
+# Citation
+If you found this dataset useful, please cite our paper
+```bibtex
+@misc{zhou2025omniworld,
+      title={OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling}, 
+      author={Yang Zhou and Yifan Wang and Jianjun Zhou and Wenzheng Chang and Haoyu Guo and Zizun Li and Kaijing Ma and Xinyue Li and Yating Wang and Haoyi Zhu and Mingyu Liu and Dingning Liu and Jiange Yang and Zhoujie Fu and Junyi Chen and Chunhua Shen and Jiangmiao Pang and Kaipeng Zhang and Tong He},
+      year={2025},
+      eprint={2509.12201},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2509.12201}, 
+}
+```

metadata/omniworld_game_metadata.csv

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+UID,Video Path,Annotation Path,Img Num,Split Num,FPS,Resolution
+0b3caf48b79d,videos/OmniWorld-Game/0b3caf48b79d,annotations/OmniWorld-Game/0b3caf48b79d,605,4,24,1280×720
+0c3cefcf3a15,videos/OmniWorld-Game/0c3cefcf3a15,annotations/OmniWorld-Game/0c3cefcf3a15,2087,31,24,1280×720
+0caf78b8eeac,videos/OmniWorld-Game/0caf78b8eeac,annotations/OmniWorld-Game/0caf78b8eeac,4735,17,24,1280×720
+0eb9b32d99e3,videos/OmniWorld-Game/0eb9b32d99e3,annotations/OmniWorld-Game/0eb9b32d99e3,964,6,24,1280×720
+15133a9a1914,videos/OmniWorld-Game/15133a9a1914,annotations/OmniWorld-Game/15133a9a1914,2011,21,24,1280×720
+15ebeda55fc9,videos/OmniWorld-Game/15ebeda55fc9,annotations/OmniWorld-Game/15ebeda55fc9,14312,47,24,1280×720
+1e527547f893,videos/OmniWorld-Game/1e527547f893,annotations/OmniWorld-Game/1e527547f893,43,1,24,1280×720
+25d6c1cf20fc,videos/OmniWorld-Game/25d6c1cf20fc,annotations/OmniWorld-Game/25d6c1cf20fc,540,4,24,1280×720
+28cfa8c9b328,videos/OmniWorld-Game/28cfa8c9b328,annotations/OmniWorld-Game/28cfa8c9b328,2619,15,24,1280×720
+37342ed0b955,videos/OmniWorld-Game/37342ed0b955,annotations/OmniWorld-Game/37342ed0b955,13398,48,24,1280×720
+4100d9519613,videos/OmniWorld-Game/4100d9519613,annotations/OmniWorld-Game/4100d9519613,160,1,24,1280×720
+4411f5f656cc,videos/OmniWorld-Game/4411f5f656cc,annotations/OmniWorld-Game/4411f5f656cc,4252,17,24,1280×720
+463bb379bd0f,videos/OmniWorld-Game/463bb379bd0f,annotations/OmniWorld-Game/463bb379bd0f,9372,33,24,1280×720
+49fe95b66e88,videos/OmniWorld-Game/49fe95b66e88,annotations/OmniWorld-Game/49fe95b66e88,2902,17,24,1280×720
+4d59e0da0f3e,videos/OmniWorld-Game/4d59e0da0f3e,annotations/OmniWorld-Game/4d59e0da0f3e,1239,14,24,1280×720
+52e80f590716,videos/OmniWorld-Game/52e80f590716,annotations/OmniWorld-Game/52e80f590716,13964,38,24,1280×720
+531f489ee104,videos/OmniWorld-Game/531f489ee104,annotations/OmniWorld-Game/531f489ee104,7305,24,24,1280×720
+54911337de15,videos/OmniWorld-Game/54911337de15,annotations/OmniWorld-Game/54911337de15,3322,9,24,1280×720
+55569a86e022,videos/OmniWorld-Game/55569a86e022,annotations/OmniWorld-Game/55569a86e022,4382,25,24,1280×720
+57a260659876,videos/OmniWorld-Game/57a260659876,annotations/OmniWorld-Game/57a260659876,7474,84,24,1280×720
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