Datasets:
language:
- en
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 |
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 | (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
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.
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.