# Predicting the Physical Dynamics of Unseen 3D Objects

arXiv: https://arxiv.org/abs/2001.06291 · HTML: https://mlsanity.com/paper/2001.06291/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion](https://mlsanity.com/paper/2308.16905/) (2023)
- [CG-HOI: Contact-Guided 3D Human-Object Interaction Generation](https://mlsanity.com/paper/2311.16097/) (2023)
- [No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces](https://mlsanity.com/paper/2203.11113/) (2022)
- [Pretraining on Interactions for Learning Grounded Affordance Representations](https://mlsanity.com/paper/2207.02272/) (2022)
- [Probabilistic Programming Bots in Intuitive Physics Game Play](https://mlsanity.com/paper/2104.01980/) (2021)
- [CoopDiff: Anticipating 3D Human-object Interactions via Contact-consistent Decoupled Diffusion](https://mlsanity.com/paper/2508.07162/) (2025)

## Abstract (arXiv)

Machines that can predict the effect of physical interactions on the dynamics of previously unseen object instances are important for creating better robots and interactive virtual worlds. In this work, we focus on predicting the dynamics of 3D objects on a plane that have just been subjected to an impulsive force. In particular, we predict the changes in state - 3D position, rotation, velocities, and stability. Different from previous work, our approach can generalize dynamics predictions to object shapes and initial conditions that were unseen during training. Our method takes the 3D object's shape as a point cloud and its initial linear and angular velocities as input. We extract shape features and use a recurrent neural network to predict the full change in state at each time step. Our model can support training with data from both a physics engine or the real world. Experiments show that we can accurately predict the changes in state for unseen object geometries and initial conditions.