Machines have become remarkably good at perceiving motion. Given a video, modern models can track how objects and points move through a scene with exceptionally high confidence. But perception is inherently retrospective: it explains motion that has already happened. Many of the systems and applications we want to build need to look forward instead. A robot reaching for a cup has to anticipate how the cup will move before it touches it. A video generator has to know what realistic motion comes next if it's going to produce physically plausible frames.

Predicting motion is harder than observing it, but it's also far more useful in many scenarios.

This idea was the motivation behind MolmoMotion, a new motion forecasting model we're releasing today. Given a video frame, 3D points marked on an object, and written instructions describing the intended action (e.g., “Move and rotate the wooden bowl with fruit on the table”), MolmoMotion predicts where those points will move over the next few seconds in 3D space—achieving substantially stronger performance than existing forecasting methods.

Given an RGB observation, a set of query points on an object, and an action description, MolmoMotion predicts the object's future 3D point trajectory. These predicted trajectories can then drive downstream applications such as robotics planning and trajectory-conditioned video generation.

Alongside the model, we're publishing MolmoMotion-1M, the largest collection of 3D point trajectories paired with action descriptions, drawn from 1.16M videos. We're also releasing PointMotionBench, a human-validated benchmark designed to measure object-centric 3D motion forecasting accuracy, containing 2.7K video clips.

We find that motion forecasters like MolmoMotion can be useful across a range of downstream tasks, from robot planning to controllable video generation. We're releasing the model weights, the MolmoMotion-1M dataset, and our PointMotionBench benchmark openly for the community to study, improve, and customize.