# Disentangled Dynamic Representations from Unordered Data

## TL;DR

This paper introduces a deep generative model that learns to disentangle static and dynamic features from unordered data, enabling better understanding of data dynamics regardless of sequence order.

## Contribution

The method uniquely exploits order-invariant regularities in sequential data to learn disentangled representations, advancing the modeling of dynamic data from unordered inputs.

## Key findings

- Effective on synthetic dynamic datasets
- Successfully applied to real video data
- Produces coherent latent space for data dynamics

## Abstract

We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data that exist regardless of the order in which the data is viewed. The result of our factorized graphical model is a well-organized and coherent latent space for data dynamics. We demonstrate our method on several synthetic dynamic datasets and real video data featuring various facial expressions and head poses.