Game Sprite Generator Using a Multi Discriminator GAN

Abstract

This paper proposes an image generation method using a Multi Discriminator Generative Adversarial Net (MDGAN) as a next generation 2D game sprite creation technique. The proposed GAN is an Autoencoder-based model that receives three areas of information-color, shape, and animation, and combines them into new images. This model consists of two encoders that extract color and shape from each image, and a decoder that takes all the values of each encoder and generates an animated image. We also suggest an image processing technique during the learning process to remove the noise of the generated images. The resulting images show that 2D sprites in games can be generated by independently learning the three image attributes of shape, color, and animation. The proposed system can increase the productivity of massive 2D image modification work during the game development process. The experimental results demonstrate that our MDGAN can be used for 2D image sprite generation and modification work with little manual cost.

Study on 2D Sprite *3.Generation Using the Impersonator Network

“…In particular, studies on increasing the efficiency of game resource production are receiving considerable attention. Studies continue to investigate game character-related production and level content, such as 2D face sprite generation [9,10] and 3D face generation [11,12]. However, the generation of animation images is limited by the fact that generated images are not guaranteed to have spatiotemporal continuity.…”

Semi-Supervised Spatial Attention Method for Facial Attribute Editing

“…been implemented successfully in the fields of facial attribute editing [2][3][4][5][6][7][8], image superresolution [14], 2-D game sprite generation [15], and representation learning [16].…”

“…Subsequently, the average of the estimated attention mask values 𝒎𝒎 � a activated in the immutable area is measured as the nontarget suppression loss, which can suppress unnecessary changes. The nontarget suppression loss can be obtained using Equation (15):…”