Duplicate from EmbodiedCity/AirScape-Dataset

Co-authored-by: BN Zhao <trainandtest666@users.noreply.huggingface.co>

.gitattributes

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+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+task_categories:
+- summarization
+language:
+- en
+tags:
+- Video
+- Text
+- Embodied
+size_categories:
+- 10K<n<100K
+configs:
+  - config_name: benchmark
+    data_files: prompt.parquet
+---
+
+# [ACM MM'25] AirScape: An Aerial Generative World Model with Motion Controllability
+This repository contains the dataset introduced in the paper, consisting of two parts: 11k+ motion intention prompts and corresponding video clips.
+
+- **Arxiv**: https://arxiv.org/pdf/2507.08885
+- **Project**: https://embodiedcity.github.io/AirScape/
+- **Code**: https://github.com/EmbodiedCity/AirScape.code
+
+## Dataset Description
+This dataset is proposed for training and testing of aerial world models. 
+You can use the dataset to train or evaluate the capability of the World Foundation Models to predict future embodied observations based on motion intentions.
+
+- **Video Data**: Stored in the `train` and `test` folder.
+- **Text Data**: Available in the file `prompt.parquet`. The `prompt.parquet` contains the following fields:
+
+
+| Field Name         | Description                          |
+|:-------------------|:-------------------------------------|
+| `video_id`         | Video name for each motion intention |
+| `set`              | Train set of test set                |
+| `motion_intention` | Motion intention                     |
+
+The proposed dataset exhibits diversity across multiple dimensions, including actions, areas, scenes, and tasks, as illustrated by the examples below.
+
+### Action
+
+
+|               |                                            Video                                             |                                                                                                                 Motion Intention                                                                                                                  |
+|:-------------:|:--------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| **Translation**  | <img src="assets/dataset_example/action/05638_WebUAV3M_train_minibus_9_1.gif" width="100%"/> |                                      The drone moves rightward while capturing a video of cars moving along the bridge, keeping the bridge centered in its field of view without obvious gimbal adjustment.                                       |
+| **Rotation**  |      <img src="assets/dataset_example/action/00001_urbanvideo_train.gif" width="100%"/>      |                                                                                 The drone rotates to the right and maintains a steady altitude and camera angle.                                                                                  |
+| **Compound Movement**  |      <img src="assets/dataset_example/action/00388_urbanvideo_train.gif" width="100%"/>      | The drone, while maintaining its altitude, continuously flies forward towards the parking lot and adjusts its gimbal downward to an overhead view, capturing a top-down view of the parked cars, and eventually stabilizes above the parking lot. |
+
+
+
+
+### Area
+
+|                        |                                                Video                                                | Motion Intention |
+|:----------------------:|:---------------------------------------------------------------------------------------------------:|:------:|
+|      **Roadside**      |          <img src="assets/dataset_example/area/00109_urbanvideo_train.gif" width="100%"/>           | The drone moved forward steadily while maintaining altitude. Meanwhile, the drone's gimbal has been adjusted downwards to a 45 degree oblique view, capturing a descending viewpoint of a busy urban road surrounded by buildings and vehicles, and ended its final position directly over the road. |
+| **Tourist Attraction** | <img src="assets/dataset_example/area/05442_WebUAV3M_train_hot-air_balloon_83_0.gif" width="100%"/> | The drone follows the red heart-shaped hot air balloon, gradually rotating leftward, maintaining focus on the balloon without camera gimbal adjustments. |
+|      **Seaside**       |      <img src="assets/dataset_example/area/05446_WebUAV3M_train_hotel_1_1.gif" width="100%"/>       | The drone flies forward while keeping the four buildings in the field of view, no significant altitude change and camera movements. |
+
+### Scene
+
+|              |                                                Video                                                | Motion Intention |
+|:------------:|:---------------------------------------------------------------------------------------------------:|:------:|
+|  **Night**   |  <img src="assets/dataset_example/scene/01909_NAT2021_train_0138tricycle1_3_0.gif" width="100%"/>   | The drone flies forward while maintaining its focus on the bridge and the cars below, keeping the camera gimbal stable and centered on the scene. |
+| **Daytime**  | <img src="assets/dataset_example/scene/04170_WebUAV3M_train_container_ship_11_5.gif" width="100%"/> | The drone flies forward while keeping both the Maersk container ship and the tugboat in its field of view, with the camera gimbal slightly adjusting to track them continuously. |
+| **Snowy** |   <img src="assets/dataset_example/scene/09048_WebUAV3M_train_snowmobile_29_1.gif" width="100%"/>   | The drone flies upwards and slightly backward while rotating left, keeping a snowmobile in its field of view as it moves around the edge of a forested area on a snow-covered landscape. |
+
+### Task
+
+|                |                                                      Video                                                      |                                                                                                                               Motion Intention                                                                                                                                |
+|:--------------:|:---------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| **Navigation** |                 <img src="assets/dataset_example/task/00925_urbanvideo_test.gif" width="100%"/>                 | The drone moved forward steadily and turned right slightly, maintaining its altitude, with the camera gimbal slightly tilted downward, capturing a street view with parked cars, trees, buildings, and pedestrians, before coming to a stop over a commercial area. |
+|  **Tracking**  | <img src="assets/dataset_example/task/04689_WebUAV3M_train_gaily-painted_pleasure-boat_10_2.gif" width="100%"/> |                          The drone follows a boat moving forward along the river, maintaining a steady distance while adjusting its position slightly to the left and aligning the camera to keep the boat centered in the field of view.                           |
+| **Detection**  |            <img src="assets/dataset_example/task/08079_WebUAV3M_train_sedan_67_1.gif" width="100%"/>            |     The drone flies forward while detecting traffic flow on the road.                                                                                                  |
+
+## Acknowledgement
+The drone video data included in this dataset is sourced from three open collections (**All video copyrights belong to their respective dataset owners**):
+
+- **UrbanVideo-Bench**  
+  - URL: https://huggingface.co/datasets/EmbodiedCity/UrbanVideo-Bench  
+  - License: MIT License
+
+- **NAT2021**  
+  - URL: https://vision4robotics.github.io/NAT2021/  
+  - License: Apache License 2.0  
+  - Notes: Please comply with all terms of the Apache License, Version 2.0
+    (include a copy of the license and any required NOTICE file, 
+    state your modifications, etc.).
+
+- **WebUAV-3M**  
+  - URL: https://github.com/983632847/WebUAV-3M?tab=readme-ov-file  
+  - License: GNU General Public License v3  
+  - Notes: Please comply with all terms of the GNU GPL v3 
+    (distribute derivative works under GPL v3, include the full license text, 
+    mark modified files, preserve the warranty disclaimer, etc.).
+
+
+
+## Citation
+
+```bibtex
+@inproceedings{zhao2025airscape,
+  author    = {Baining Zhao and Rongze Tang and Mingyuan Jia and Ziyou Wang and Fanhang Man and Xin Zhang and Yu Shang and Weichen Zhang and Wei Wu and Chen Gao and Xinlei Chen and Yong Li},
+  title     = {AirScape: An Aerial Generative World Model with Motion Controllability},
+  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia (MM '25)},
+  year      = {2025},
+  month     = {October},
+  pages     = {1--10},
+  address   = {Dublin, Ireland},
+  publisher = {ACM},
+  location  = {New York, NY, USA},
+  doi       = {10.1145/3746027.3758180},
+  url       = {https://doi.org/10.1145/3746027.3758180}
+}
+```

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