Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art
Not on the map. Why?
Abstract (arXiv)
Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets, such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for purposes outside of games. The body of research, focused on specific methods for generating specific assets, provides a narrow view of the available possibilities. Hence, it is difficult to have a clear picture of all approaches and possibilities, with no guide for interested parties to discover possible methods and approaches for their needs, and no facility to guide them through each technique or approach to map out the process of using them. Therefore, a systematic literature review has been conducted, yielding 200 accepted papers. This paper explores state-of-the-art approaches to graphical asset generation, examining research from a wide range of applications, inside and outside of games. Informed by the literature, a conceptual framework has been derived to address the aforementioned gaps.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 107 of its 308 references that are in the map. First posted on arXiv 16 Nov 2023.
Builds on
107 of 308 references.
References · 107
- 2017Attention Is All You Needcited by 60,361
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2017Mask R-CNNcited by 7,277
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2021Zero-Shot Text-to-Image Generationcited by 4,288
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
38 more
- 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
- 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,295
- 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
- 2020Convolutional Occupancy Networkscited by 761
- 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 694
- 2020Image Segmentation Using Deep Learning: A Surveycited by 660
- 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
- 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 553
- 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
- 2015A Neural Algorithm of Artistic Stylecited by 483
- 2019Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Setcited by 482
- 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 460
- 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
- 2017Neural 3D Mesh Renderercited by 437
- 2023Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priorscited by 397
- 2022Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusioncited by 309
- 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
- 2021CLIP-Forge: Towards Zero-Shot Text-to-Shape Generationcited by 254
- 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 218
- 2022AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generationcited by 208
- 2021ATISS: Autoregressive Transformers for Indoor Scene Synthesiscited by 200
- 2019Controllable Text-to-Image Generationcited by 187
- 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
- 20193D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutionscited by 173
- 2023DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesiscited by 166
- 2020SceneFormer: Indoor Scene Generation with Transformerscited by 158
- 2023Infinite Photorealistic Worlds using Procedural Generationcited by 147
- 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
- 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
- 2019SDM-NET: Deep Generative Network for Structured Deformable Meshcited by 113
- 2019Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networkscited by 106
- 2022SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generationcited by 92
- 2020PCGRL: Procedural Content Generation via Reinforcement Learningcited by 90
- 2020House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generationcited by 87
- 2020Discrete Point Flow Networks for Efficient Point Cloud Generationcited by 87
- 2020Deep Learning for Procedural Content Generationcited by 73
- 2017Variational Autoencoders for Deforming 3D Mesh Modelscited by 70
50 most cited of 107 All 107 (JSON)
Built on by
Citing papers per year: 2025: 1.
1 paper.