Automatic Video Colorization Using 3D Conditional Generative Adversarial Networks

  • Panagiotis Kouzouglidis
  • Giorgos SfikasEmail author
  • Christophoros Nikou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11844)


In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner. Network convolutional and deconvolutional layers are three-dimensional, with frame height, width and time as the dimensions taken into account. Multiple chrominance estimates per frame are aggregated and combined with available luminance information to recreate a colored sequence. Colorization trials are run successfully on a dataset of old black-and-white films. The usefulness of our method is also validated with numerical results, computed with a newly proposed metric that measures colorization consistency over a frame sequence.


Video colorization Generative Adversarial Networks Three-dimensional convolution Black-and-white films 



We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan XP GPU used for this research.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Panagiotis Kouzouglidis
    • 1
  • Giorgos Sfikas
    • 1
    • 2
    Email author
  • Christophoros Nikou
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of IoanninaIoanninaGreece
  2. 2.Information Technologies Institute, CERTHThessalonikiGreece

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