---
language:
- en
license: cc-by-nc-sa-4.0
pipeline_tag: image-to-video
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-paper-b31b1b)](https://arxiv.org/abs/2604.11737)
[![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}
}
```