Update README.md

README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+language:
+- en
+license: cc-by-nc-sa-4.0
+pipeline_tag: other
+tags:
+- motion-generation
+- trajectory-prediction
+- robotics
+- computer-vision
+- pytorch
+- torch-hub
+---
+
+# ZipMo (Learning Long-term Motion Embeddings for Efficient Kinematics Generation)
+
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://compvis.github.io/long-term-motion)
+[![Paper](https://img.shields.io/badge/arXiv-coming_soon-b31b1b)](https://arxiv.org/)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/CompVis/long-term-motion)
+[![Venue](https://img.shields.io/badge/CVPR-2026-green)](https://compvis.github.io/long-term-motion)
+
+ZipMo is a motion-space model for efficient long-horizon kinematics generation. It learns compact long-term motion embeddings from large-scale tracker-derived trajectories and generates plausible future motion directly in this learned motion space. The model supports spatial-poke conditioning for open-domain videos and task/text-embedding conditioning for LIBERO robotics evaluation.
+
+## Paper and Abstract
+
+ZipMo was introduced in the CVPR 2026 paper **Learning Long-term Motion Embeddings for Efficient Kinematics Generation**.
+
+Understanding and predicting motion is a fundamental component of visual intelligence. Although video models can synthesize scene dynamics, exploring many possible futures through full video generation is expensive. ZipMo instead operates directly on long-term motion embeddings learned from tracker trajectories, enabling efficient generation of long, realistic motions while preserving dense reconstruction at arbitrary spatial query points.
+
+![ZipMo teaser figure](https://compvis.github.io/long-term-motion/static/images/social_preview.png)
+*ZipMo generates long-horizon motion in a compact learned motion space, supporting spatial-poke conditioning for open-domain videos and task-conditioned action prediction on LIBERO.*
+
+## Usage
+
+For programmatic use, the simplest way to use ZipMo is via `torch.hub`:
+
+```python
+import torch
+
+repo = "CompVis/long-term-motion"
+
+# Open-domain motion prediction
+planner_sparse = torch.hub.load(repo, "zipmo_planner_sparse")
+planner_dense = torch.hub.load(repo, "zipmo_planner_dense")
+
+# Motion autoencoder
+vae = torch.hub.load(repo, "zipmo_vae")
+```
+
+LIBERO planning and policy components can be loaded in the same way:
+
+```python
+import torch
+
+repo = "CompVis/long-term-motion"
+
+# LIBERO planners
+libero_atm_planner = torch.hub.load(repo, "zipmo_planner_libero", "atm")
+libero_tramoe_planner = torch.hub.load(repo, "zipmo_planner_libero", "tramoe")
+
+# LIBERO policy heads
+policy_head_atm = torch.hub.load(repo, "zipmo_policy_head", "atm")
+policy_head_tramoe_goal = torch.hub.load(repo, "zipmo_policy_head", "tramoe", "goal")
+```
+
+Available Torch Hub entries:
+
+- `zipmo_planner_sparse`: sparse-poke planner for open-domain motion prediction.
+- `zipmo_planner_dense`: dense-conditioning planner for open-domain motion prediction.
+- `zipmo_vae`: long-term motion autoencoder.
+- `zipmo_planner_libero`: LIBERO planner with mode `atm` or `tramoe`.
+- `zipmo_policy_head`: LIBERO policy head with mode `atm` or `tramoe`. For `tramoe`, pass one of `10`, `goal`, `object`, or `spatial`.
+
+For the interactive demo, standard track prediction evaluation, LIBERO rollout evaluation, and training instructions, see the [GitHub repository](https://github.com/CompVis/long-term-motion).
+
+## Citation
+
+If you find our model or code useful, please cite our paper:
+
+```bibtex
+@inproceedings{stracke2026motionembeddings,
+  title     = {Learning Long-term Motion Embeddings for Efficient Kinematics Generation},
+  author    = {Stracke, Nick and Bauer, Kolja and Baumann, Stefan Andreas and Bautista, Miguel Angel and Susskind, Josh and Ommer, Bj{\"o}rn},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year      = {2026}
+}
+```