| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # MoSE3: Learning World-Space SE(3) at Every Pixel |
|
|
| [Paper](https://arxiv.org/abs/2610.03716) | [Project page](https://mose3-tracker.github.io/) | [Code](https://github.com/mose3-tracker/MoSE3) |
|
|
| **MoSE3** is a feed-forward model that predicts world-space **SE(3) motion for every pixel** of a |
| monocular RGB video, generalizing across **rigid, articulated and deformable objects**. |
|
|
| ## Usage |
|
|
| ```python |
| from mose3.models.mose3 import MoSE3 |
| |
| model = MoSE3.from_pretrained("mose3-tracker/MoSE3", strict=True).cuda().eval() |
| ``` |
|
|
| The [code repository](https://github.com/mose3-tracker/MoSE3) has the full inference and visualization scripts. |
|
|
| ## License |
|
|
| The weights are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| (non-commercial). They include the weights of π³, which are released under the same license. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{cheng2026mose3, |
| title = {{MoSE3}: Learning World-Space {SE(3)} at Every Pixel}, |
| author = {Cheng, Jiahuan and Li, Zhiyi and Xia, Tian and |
| Cai, Ruojin and Du, Yilun and Wang, Qianqian}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, |
| year = {2026} |
| } |
| ``` |
|
|
