--- 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. ![image/png](imgs/dataset.png) ### 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 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** 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. ## Citation ```bibtex @article{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}, journal = {arXiv preprint arXiv:2412.07759}, year = {2024} } ``` ## Contact Xiao Fu: lemonaddie0909@gmail.com