Broad, Terence and Grierson, Mick. 2016. Autoencoding Video Frames. Technical Report. Goldsmiths, London. [Report]

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Abstract or Description

This report details the implementation of an autoencoder trained with a learned similarity metric - one that is capable of modelling a complex dis- tribution of natural images - training it on frames from selected films, and using it to reconstruct video sequences by passing each frame through the autoencoder and re-sequencing the output frames in-order. This is primarily an artistic exploration of the representational capacity of the current state of the art in generative models and is a novel application of autoencoders. This model is trained on, and used to reconstruct the films Blade Runner and A Scanner Darkly, producing new artworks in their own right. Experiments passing other videos through these models is carried out, demonstrating the potential of this method to become a new technique in the production of experimental image and video.

Item Type:	Report (Technical Report)
Departments, Centres and Research Units:	Computing
Date:	12 May 2016
Item ID:	19559
Date Deposited:	27 Jan 2017 17:57
Last Modified:	29 Apr 2020 16:23
URI:	https://research.gold.ac.uk/id/eprint/19559

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