# Unsupervised Separation of Dynamics from Pixels

## TL;DR

This paper introduces an unsupervised probabilistic model that learns object dynamics from image sequences by combining linear state-space models with non-linear rendering, enabling efficient inference and separation of object motions.

## Contribution

It presents a novel probabilistic framework that separates object dynamics from pixel data using linear models and non-linear rendering, allowing flexible inference without retraining.

## Key findings

- Effective separation of object dynamics from images
- Efficient inference enabled by linear state-space models
- Applicable to multiple objects in image sequences

## Abstract

We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way. We introduce a probabilistic model that first generate noisy positions for each object through a separate linear state-space model, and then renders the positions of all objects in the same image through a highly non-linear process. Such a linear representation of the dynamics enables us to propose an inference method that uses exact and efficient inference tools and that can be deployed to query the model in different ways without retraining.