A Case Study of Generative Adversarial Networks for Procedural Synthesis of Original Textures in Video Games

Fadaeddini, Amin; Majidi, Babak; Eshghi, Mohammad

doi:10.1109/dgrc.2018.8712070

Cited by 20 publications

(6 citation statements)

References 16 publications

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“…Different from above work, Fontaine et al [29] proposed latent space illumination (LSI), which uses quality diversity algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME) [28], to search the latent space of trained generators, aiming at increasing the diversity of generated levels. Besides generating 2D and 3D levels represented as pixel-based or tile-based images, texture [25] and sprite generation [48] have also been investigated. Hong et al [48] generated 2D image sprites using a multi-discriminator GAN, in which two encoders were used for bone graph, shape and color, without sharing parameters.…”

Section: Adversarial Learningmentioning

confidence: 99%

Deep Learning for Procedural Content Generation

Liu,

Snodgrass,

Khalifa

et al. 2020

Preprint

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Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been

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Section: Adversarial Learningmentioning

confidence: 99%

Deep Learning for Procedural Content Generation

Liu,

Snodgrass,

Khalifa

et al. 2020

Preprint

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“…From the first introduction of Generative Adversarial Networks in 2014, GANs have been a focus of attention in generative machine learning (according to Google scholar, there are around 75000 papers based or focusing on GANs to date 2 ). GANs have predominantly been used in computer vision, including but not limited to image generation, face synthesis [8], image translation [9,10,11], texture synthesis [12,13], medical imaging, [14] and super-resolution [15]. Moreover, GANs can be applied in many other fields including but not limited to voice and speech signals [16,17,18], anomaly detection [19], power systems and smart grids [20,21,22], electronics [23,24], and fault diagnosis [25,26,27,28].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

et al. 2021

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“…While the others, e.g. automatic model placement [4], [6], and automatic coloring [19], remain underexplored problems [5].…”

Section: Introductionmentioning

confidence: 99%

HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population

Kostiuk¹,

Stachura²,

Tadeja³

et al. 2021

Preprint

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Designing a 3D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves synthesis, coloring, and placement of 3D models within the game scene. To lessen this workload, we can apply machine learning to automate some aspects of the game scene development. Earlier research has already tackled automated generation of the game scene background with machine learning. However, model auto-coloring remains an underexplored problem. The automatic coloring of a 3D model is a challenging task, especially when dealing with the digital representation of a colorful, multipart object. In such a case, we have to "understand" the object's composition and coloring scheme of each part. Existing single-stage methods have their own caveats such as the need for segmentation of the object or generating individual parts that have to be assembled together to yield the final model. We address these limitations by proposing a two-stage training approach to synthesize auto-colored 3D models. In the first stage, we obtain a 3D point cloud representing a 3D object, whilst in the second stage, we assign colors to points within such cloud. Next, by leveraging the so-called triangulation trick, we generate a 3D mesh in which the surfaces are colored based on interpolation of colored points representing vertices of a given mesh triangle. This approach allows us to generate a smooth coloring scheme. Experimental evaluation shows that our two-stage approach gives better results in terms of shape reconstruction and coloring when compared to traditional single-stage techniques.

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A Case Study of Generative Adversarial Networks for Procedural Synthesis of Original Textures in Video Games

Cited by 20 publications

References 16 publications

Deep Learning for Procedural Content Generation

Deep Learning for Procedural Content Generation

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population

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