AHCI RESEARCH GROUP

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2025

Kammari, K. S.; Annambhotla, Y. L.; Khanna, M.

ProWGAN a hybrid generative adversarial network for automated landscape generation in media and video games Journal Article

In: Discover Artificial Intelligence, vol. 5, no. 1, 2025, ISSN: 27310809 (ISSN), (Publisher: Springer Nature).

Abstract | Links | BibTeX | Tags: 'current, 3D modeling, Adversarial networks, Generative AI, Hybrid model, Image production, Interactive computer graphics, Landscape, Landscapes, Motion pictures, Progressive GAN, Video-games, Videogame environment, Videogames environments, Virtual Reality, Wasserstein GAN

@article{kammari_prowgan_2025,

title = {ProWGAN a hybrid generative adversarial network for automated landscape generation in media and video games},

author = {K. S. Kammari and Y. L. Annambhotla and M. Khanna},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017739103&doi=10.1007%2Fs44163-025-00512-5&partnerID=40&md5=5e48fe6941113d9196abb4308cf5db0f},

doi = {10.1007/s44163-025-00512-5},

issn = {27310809 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Discover Artificial Intelligence},

volume = {5},

number = {1},

abstract = {The current approaches of creating realistic, high-quality landscape imagery mostly depend on labor-intensive manual design procedures. In an effort to simplify image production for video games, virtual reality, and motion pictures, a new hybrid model called ProWGAN, combining ProGAN and WGAN approaches, is employed for automated landscape synthesis. Five models (FCGAN, DCGAN, ProGAN, WGAN, and ProWGAN) were trained on a dataset of landscape images and compared using multiple evaluation metrics. Compared to traditional models, ProWGAN produces 128-128 size images with the best FID score (29.67), IS (5.11), and lowest critic loss (0.2), fully capturing landscape features in just 5 h of training and 50 epochs. The layered method to producing images and progressive learning of ProGAN with the stability of WGAN’s Wasserstein distance showed superior ability to generate realistic landscape images. The results demonstrate how ProWGAN can revolutionize landscape image production by reducing manual work, lowering production time and effort and how a 2d image can be converted into 3d model via MeshRoom. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Springer Nature},

keywords = {'current, 3D modeling, Adversarial networks, Generative AI, Hybrid model, Image production, Interactive computer graphics, Landscape, Landscapes, Motion pictures, Progressive GAN, Video-games, Videogame environment, Videogames environments, Virtual Reality, Wasserstein GAN},

pubstate = {published},

tppubtype = {article}

}