3D Character Generation using PCGML
Balika J. Chelliah1, Vijay Krishna Vallabhaneni2, Saikiran Reddy Lenkala3, Mithran J4, M Kesava Krishna Reddy5
1Balika J. Chelliah, Associate Professor, Undergraduate Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
2Vijay Krishna Vallabhaneni, Undergraduate Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
3Saikiran Reddy Lenkala, Undergraduate Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
4Mithran J, Undergraduate Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
5M Kesava Krishna Reddy, Undergraduate Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
Manuscript received on 04 April 2019 | Revised Manuscript received on 11 April 2019 | Manuscript Published on 26 April 2019 | PP: 105-109 | Volume-8 Issue-6S April 2019 | Retrieval Number: F60320486S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In video games, both the characters and the world play an important role in creating a sense of immersion to the player. Although each character can be modelled by hand to make them feel more life-like, the process becomes inherently complex when populating a game map with more than 100000+ characters. In cases like this, developers must look towards incorporating new tools to automate and accelerate their creation pipeline. We present a new method to procedurally generate the Non- Playable Characters (NPCs) in video games using a modified Style-based generative adversarial network (StyleGAN) which is a type of neural network. PCGML acronym for Procedural Content Generation using Machine learning is the most cost- effective method for game content generation, it is employed to reduce production effort and to save storage space. Our approach adapts the use of PCGML with styleGAN to generate NPCs that are unique in both appearance and behaviour. The properties or traits influence the generation of characters making the game environment diverse and interesting for the players.
Keywords: Procedural Content Generation, Machine Learning, StyleGAN, Video Games, Characters, 3D.
Scope of the Article: Machine Learning