Datasets:
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---
language:
- en
---
# 360°-Motion Dataset
[Project page](http://fuxiao0719.github.io/projects/3dtrajmaster) | [Paper](https://drive.google.com/file/d/111Z5CMJZupkmg-xWpV4Tl4Nb7SRFcoWx/view)
### News
- [2024-12] We release the V1 dataset (36,000 videos consists of 50 entities, 6 UE scenes, and 121 trajectory templates).
### Data structure
```
├── 360Motion-Dataset Video Number Cam-Obj Distance (m)
├── Desert (`desert`) 18,000
├── location_data.json
├── HDRI [3.43, 13.01]
├── loc1 (`snowy street`) 3,600
├── loc2 (`park`) 3,600
├── loc3 (`indoor open space`) 3,600
├── loc11 (`gymnastics room`) 3,600
├── loc13 (`autumn forest`) 3,600
├── location_data.json
├── RefPic
├── CharacterInfo.json
├── Hemi12_transforms.json
```
**(1) Released Dataset Information**
| Argument | Description |Argument | Description |
|-------------------------|-------------|-------------------------|-------------|
| **Video Resolution** | 480×720 | **Frames/Duration/FPS** | 99/3.3s/30 |
| **UE Scenes** | 6 (1 desert+5 HDRIs) | **Video Samples** | 36,000 |
| **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}|
**(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 1) still under our internal license check, 2) awaiting the paper decision.
| Argument | Released Dataset | Our Internal Dataset|
|-------------------------|-------------|-------------------------|
| **Video Resolution** | 480×720 (re-rendered) | 384×672 |
| **Entities** | 50 (all animals) | 70 (20 humans+50 animals) |
| **Video Samples** | 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.
<img src="imgs/vis_objstraj.png" width="350" /> |