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Title:Structural consistency for diverse video colorization
Author(s):Wang, Alan
Contributor(s):Schwing, Alexander
Variational Autoencoder
Gaussian Conditional Random Field
Abstract:Colorizing gray-level videos is an important task in the media and advertising industry. Intelligently learning believable and structurally-consistent colorings over large, intractable video spaces poses several problems. Firstly, there is a lack of proper datasets for training. Secondly, there is ambiguity inherent in colorization due to many shades being often plausible. Also, one of the most obvious artifacts, structural inconsistency, is rarely considered by existing methods which predict chrominance independently for every pixel. We address all of the above-mentioned challenges in two ways. First, we generate a diverse video colorization dataset by editing scenes and manipulating textures from the Grand Theft Auto V video game. Second, we propose models for diverse and structurally-consistent video colorization, which uses a conditional random field based variational autoencoder formulation (VAEGCRF). We show our results using the generated dataset, and compare them to several baseline models.
Issue Date:2019-05
Date Available in IDEALS:2019-06-17

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