---
title: 360Motion-Dataset
canonical_url: "https://www.modelscope.cn/datasets/KwaiVGI/360Motion-Dataset"
md_url: "https://www.modelscope.cn/datasets/KwaiVGI/360Motion-Dataset.md"
repository: KwaiVGI/360Motion-Dataset
last_updated: 2025-09-04
license: apache-2.0
storage_size: "62 GB"
downloads: 250
stars: 0
---

# 360Motion-Dataset

> 360Motion-Dataset - KwaiVGI 在 ModelScope 开源的数据集。Project page | Paper | Code

KwaiVGI/360Motion-Dataset 是 ModelScope 魔搭社区上的数据集，存储大小 62 GB，采用 apache-2.0 许可。

- **Repository**: KwaiVGI/360Motion-Dataset
- **License**: apache-2.0
- **Storage size**: 62 GB
- **Downloads**: 250
- **Stars**: 0
- **Last updated**: 2025-09-04

Source: https://www.modelscope.cn/datasets/KwaiVGI/360Motion-Dataset

---

# 360°-Motion Dataset

[Project page](http://fuxiao0719.github.io/projects/3dtrajmaster) | [Paper](https://drive.google.com/file/d/111Z5CMJZupkmg-xWpV4Tl4Nb7SRFcoWx/view) | [Code](https://github.com/kwaiVGI/3DTrajMaster)

### Acknowledgments
We thank Jinwen Cao, Yisong Guo, Haowen Ji, Jichao Wang, and Yi Wang from Kuaishou Technology for their help in constructing our 360°-Motion Dataset.

![image/png](imgs/dataset.png)

### News
- [2024-12] We release the V1 dataset (72,000 videos consists of 50 entities, 6 UE scenes, and 121 trajectory templates).
  
### Data structure

 ```
  ├── 360Motion-Dataset                      Video Number        Cam-Obj Distance (m)
    ├── 480_720/384_672
        ├── Desert (desert)                    18,000               [3.06, 13.39]
            ├── location_data.json
        ├── HDRI                                                      
            ├── loc1 (snowy street)             3,600               [3.43, 13.02]
            ├── loc2 (park)                     3,600               [4.16, 12.22]
            ├── loc3 (indoor open space)        3,600               [3.62, 12.79]
            ├── loc11 (gymnastics room)         3,600               [4.06, 12.32]
            ├── loc13 (autumn forest)           3,600               [4.49  11.91]
            ├── location_data.json
        ├── RefPic
        ├── CharacterInfo.json
        ├── Hemi12_transforms.json
  ```

**(1) Released Dataset Information**

| Argument                | Description |Argument                | Description |
|-------------------------|-------------|-------------------------|-------------|
| **Video Resolution**    | (1) 480×720 (2) 384×672    |       **Frames/Duration/FPS**        | 99/3.3s/30  |
| **UE Scenes**    | 6 (1 desert+5 HDRIs)  |       **Video Samples**        | (1) 36,000 (2) 36,000 |
| **Camera Intrinsics (fx,fy)**    | (1) 1060.606 (2) 989.899 |       **Sensor Width/Height (mm)**        | (1) 23.76/15.84 (2) 23.76/13.365 |
| **Hemi12_transforms.json**    | 12 surrounding cameras |      **CharacterInfo.json**        | entity prompts  |
| **RefPic**    | 50 animals     |       **1/2/3 Trajectory Templates**       | 36/60/35 (121 in total) |
| **{D/N}_{locX}** | {Day/Night}_{LocationX} |  **{C}_ {XX}_{35mm}** | {Close-Up Shot}_{Cam. Index(1-12)} _{Focal Length}|

**Note that** the resolution of 384×672 refers to our internal video diffusion resolution. In fact, we render the video at a resolution of 378×672 (aspect ratio 9:16), with a 3-pixel black border added to both the top and bottom.

**(2) Difference with the Dataset to Train on Our Internal Video Diffusion Model**

The release of the full dataset regarding more entities and UE scenes is still under our internal license check.

|  Argument              | Released Dataset |       Our Internal Dataset|
|-------------------------|-------------|-------------------------|
| **Video Resolution**    | (1) 480×720 (2) 384×672 |       384×672     |
| **Entities**    | 50 (all animals)     |      70 (20 humans+50 animals)  |
| **Video Samples**    | (1) 36,000 (2) 36,000   |    54,000   |
| **Scenes**    | 6  |   9 (+city, forest, asian town)  |
| **Trajectory Templates**    | 121 |   96  |

**(3) Load Dataset Sample**

1. Change root path to `dataset`. We provide a script to load our dataset (video & entity & pose sequence) as follows. It will generate the sampled video for visualization in the same folder path.

    ```bash
    python load_dataset.py
    ```

2. Visualize the 6DoF pose sequence via Open3D as follows.

    ```bash
    python vis_trajecotry.py
    ```
    After running the visualization script, you will get an interactive window like this. Note that we have converted the right-handed coordinate system (Open3D) to the left-handed coordinate system in order to better align with the motion trajectory of the video.

    <img src="imgs/vis_objstraj.png" width="350" />

## Citation

```bibtex
@inproceedings{fu20243dtrajmaster,
    author    = {Fu, Xiao and Liu, Xian and Wang, Xintao and Peng, Sida and Xia, Menghan and Shi, Xiaoyu and Yuan, Ziyang and Wan, Pengfei and Zhang, Di and Lin, Dahua},
    title     = {3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation},
    booktitle = {ICLR},
    year      = {2025}
}
```

## Contact

Xiao Fu: lemonaddie0909@gmail.com