Latent-space disentanglement with untrained generator networks for the isolation of different motion types in video data

TL;DR

This paper demonstrates that untrained generator networks can be used to disentangle and isolate different motion types in video data without pre-training, by leveraging minimal dynamic information.

Contribution

It introduces a novel method for motion disentanglement in videos using untrained networks and minimal dynamic cues, avoiding the need for large training datasets.

Findings

Effective isolation of non-linear motion types in videos

Disentanglement achieved with minimal dynamic information

No pre-training required, parameters learned from a single video

Abstract

Isolating different types of motion in video data is a highly relevant problem in video analysis. Applications can be found, for example, in dynamic medical or biological imaging, where the analysis and further processing of the dynamics of interest is often complicated by additional, unwanted dynamics, such as motion of the measurement subject. In this work, it is empirically shown that a representation of video data via untrained generator networks, together with a specific technique for latent space disentanglement that uses minimal, one-dimensional information on some of the underlying dynamics, allows to efficiently isolate different, highly non-linear motion types. In particular, such a representation allows to freeze any selection of motion types, and to obtain accurate independent representations of other dynamics of interest. Obtaining such a representation does not require any pre-training on a training data set, i.e., all parameters of the generator network are learned directly from a single video.