Dynamic Terrain Generation With Deep GANs
| dc.contributor.author | Kathan Gabani | |
| dc.contributor.author | Het Pathak | |
| dc.contributor.author | Isitbhai Thakkar | |
| dc.contributor.author | Nilesh Jain | |
| dc.contributor.author | Senthurapandi Rajendran | |
| dc.contributor.author | Dipyaman Mukherjee | |
| dc.date.accessioned | 2024-11-05T12:24:13Z | |
| dc.date.available | 2024-11-05T12:24:13Z | |
| dc.date.issued | 2024-10-28 | |
| dc.description.abstract | Exploring the realm of video game creation, procedural terrain generation has stood the test of time as a means to generate extensive new graphical content automatically. Conventional techniques often employ algorithms tailored for specific terrains, meticulously designed by human hands. This study delves into the innovative application of deep convolutional generative adversarial models (DC-GANs) for the dynamic fabrication of authentic terrain maps. Furthermore, we present an inventive methodology for feature extraction that facilitates the specification and manipulation of geographical attributes within the generated terrain. | |
| dc.identifier.citation | Gabani K., Pathak H., Thakkar I., Jain N., Rajendran S. & Mukherjee D. (2023). “Dynamic Terrain Generation with Deep GANs". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2023), pp. 61-69, MIRG | |
| dc.identifier.isbn | 978-978-767-697-4 | |
| dc.identifier.uri | https://ir.unilag.edu.ng/handle/123456789/13034 | |
| dc.language.iso | en | |
| dc.publisher | MIRG | |
| dc.relation.ispartofseries | MIRG-ICAIR 2023 | |
| dc.title | Dynamic Terrain Generation With Deep GANs | |
| dc.type | Article |