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---
language:
- en
license: apache-2.0
---
# 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.
### 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 |