Outpainting Images and Videos using GANs
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Shailendra Singh, Nainish Aggarwal, Udit Jain, Hrithik Jaiswal|
|DOI : 10.14445/22312803/IJCTT-V68I5P107|
How to Cite?
Shailendra Singh, Nainish Aggarwal, Udit Jain, Hrithik Jaiswal, "Outpainting Images and Videos using GANs," International Journal of Computer Trends and Technology, vol. 68, no. 5, pp. 24-29, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I5P107
This Outpainting paper studies the main fundamental issue of extrapolation of images or visual context like videos using deep generative models such as GANs (Generative Adversarial Networks), i.e., extending image and video borders with plausible structure and details. In addition, this seemingly simple job faces several critical technical challenges and has its unique properties. The challenging task of image and video outpainting (extrapolation) in comparison to it’s relative, inpainting (completion), received relatively little attention. So, we followed a deep learning adversarially approach which is based on training a network. The two main problems are the extension of scale and one side constraints. Extensive studies are carried out on various possible alternatives and methods connected with them. We are also exploring our method`s potential for various interesting applications that may support work in a variety of fields.
image processing, video processing, generative adversarial networks, extrapolation, outpainting.
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