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language: |
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- en |
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# 360°-Motion Dataset |
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[Project page](http://fuxiao0719.github.io/projects/3dtrajmaster) | [Paper](https://drive.google.com/file/d/111Z5CMJZupkmg-xWpV4Tl4Nb7SRFcoWx/view) |
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### News |
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- [2024-12] We release the V1 dataset (36,000 videos consists of 50 entities, 6 UE scenes, and 121 trajectory templates). |
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### Data structure |
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``` |
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├── 360Motion-Dataset Video Number Cam-Obj Distance (m) |
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├── Desert (`desert`) 18,000 |
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├── location_data.json |
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├── HDRI [3.43, 13.01] |
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├── loc1 (`snowy street`) 3,600 |
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├── loc2 (`park`) 3,600 |
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├── loc3 (`indoor open space`) 3,600 |
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├── loc11 (`gymnastics room`) 3,600 |
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├── loc13 (`autumn forest`) 3,600 |
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├── location_data.json |
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├── RefPic |
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├── CharacterInfo.json |
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├── Hemi12_transforms.json |
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``` |
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**(1) Released Dataset Information** |
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| Argument | Description |Argument | Description | |
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|-------------------------|-------------|-------------------------|-------------| |
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| **Video Resolution** | 480×720 | **Frames/Duration/FPS** | 99/3.3s/30 | |
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| **UE Scenes** | 6 (1 desert+5 HDRIs) | **Video Samples** | 36,000 | |
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| **Hemi12_transforms.json** | 12 surrounding cameras | **CharacterInfo.json** | entity prompts | |
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| **RefPic** | 50 animals | **1/2/3 Trajectory Templates** | 36/60/35 (121 in total) | |
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| **{D/N}_{locX}** | {Day/Night}_{LocationX} | **{C}_ {XX}_{35mm}** | {Close-Up Shot}_{Cam. Index(1-12)} _{Focal Length}| |
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**(2) Difference with the Dataset to Train on Our Internal Video Diffusion Model** |
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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. |
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| Argument | Released Dataset | Our Internal Dataset| |
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|-------------------------|-------------|-------------------------| |
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| **Video Resolution** | 480×720 (re-rendered) | 384×672 | |
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| **Entities** | 50 (all animals) | 70 (20 humans+50 animals) | |
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| **Video Samples** | 36,000 | 54,000 | |
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| **Scenes** | 6 | 9 (+city, forest, asian town) | |
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| **Trajectory Templates** | 121 | 96 | |
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**(3) Load Dataset Sample** |
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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. |
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```bash |
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python load_dataset.py |
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``` |
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2. Visualize the 6DoF pose sequence via Open3D as follows. |
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```bash |
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python vis_trajecotry.py |
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``` |
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After running the visualization script, you will get an interactive window like this. |
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<img src="imgs/vis_objstraj.png" width="350" /> |
