metadata

language:
  - en

360°-Motion Dataset

Project page | Paper

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)
   ├── 480_720
       ├── 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	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

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.
```
python load_dataset.py
```
Visualize the 6DoF pose sequence via Open3D as follows.
```
python vis_trajecotry.py
```
After running the visualization script, you will get an interactive window like this.