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title: MolmoMotion: Language-guided 3D motion forecasting
description: MolmoMotion is a new language-guided 3D motion forecasting model from AI2 that predicts where marked 3D points on objects will move over the next few seconds,...
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# MolmoMotion: Language-guided 3D motion forecasting

**[Hugging Face](https://daily.dev/sources/huggingface)** · 9 min read · 0 upvotes · 0 comments

## Summary

MolmoMotion is a new language-guided 3D motion forecasting model from AI2 that predicts where marked 3D points on objects will move over the next few seconds, given a video frame and a natural language action description. It uses Molmo 2 as its backbone and comes in two variants: an autoregressive version (MolmoMotion-AR) for well-defined trajectories and a flow-matching version (MolmoMotion-FM) for uncertain multi-modal futures. Alongside the model, AI2 releases MolmoMotion-1M, a dataset of 1.16M videos with object-grounded 3D point trajectories paired with action descriptions, and PointMotionBench, a 2.7K-clip human-validated benchmark. MolmoMotion outperforms existing 3D forecasting methods on the benchmark, improves robot pick-and-place success from 56% to 76.3%, and enhances motion quality in video generation tasks. Model weights, dataset, and benchmark are all openly released.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://huggingface.co/blog/allenai/molmomotion>

## Questions this post answers

### What is MolmoMotion and what does it predict?

MolmoMotion is a language-guided 3D motion forecasting model from Ai2 that predicts how 3D points marked on an object will move over the next few seconds, given a video frame, query points, and a written action description like 'move and rotate the wooden bowl.' It uses the Molmo 2 backbone and comes in autoregressive (MolmoMotion-AR) and flow-matching (MolmoMotion-FM) variants.

_Teams evaluating motion-forecasting approaches for robotics or video pipelines can track releases like MolmoMotion on daily.dev._

### How much does MolmoMotion improve robot pick-and-place success rates over a Molmo 2 baseline?

A control policy built on MolmoMotion succeeds on 76.3% of pick-and-place tasks in simulation, compared to 56.0% for the same policy built on plain Molmo 2. It also learns faster, reaching 51% success after 10K training steps versus Molmo 2's 19%, and on real robots it matches Molmo 2's 12K-step test error in only about 2K training steps.

_Robotics engineers comparing motion-forecasting backbones can follow benchmark results like these on daily.dev._

### What datasets did Ai2 release alongside MolmoMotion?

Ai2 released MolmoMotion-1M, drawn from 1.16 million videos and spanning 736 motion types and 5.6K distinct objects, described as the largest corpus of action-described, object-grounded 3D point trajectories to date. It also released PointMotionBench, a human-validated benchmark of 2.7K clips across 111 object categories and 61 motion types for evaluating 3D motion forecasting accuracy.

_Researchers building or benchmarking motion-forecasting models can keep tabs on new open datasets via daily.dev._

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---

Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#computer-vision](https://daily.dev/tags/computer-vision), [#robotics](https://daily.dev/tags/robotics), [#video-generation](https://daily.dev/tags/video-generation)

[View this post on daily.dev](https://daily.dev/posts/molmomotion-language-guided-3d-motion-forecasting-j9ihm74ru)

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