Neural Style Transfer for Computer Games
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Abstract (arXiv)
Neural Style Transfer (NST) research has been applied to images, videos, 3D meshes and radiance fields, but its application to 3D computer games remains relatively unexplored. Whilst image and video NST systems can be used as a post-processing effect for a computer game, this results in undesired artefacts and diminished post-processing effects. Here, we present an approach for injecting depth-aware NST as part of the 3D rendering pipeline. Qualitative and quantitative experiments are used to validate our in-game stylisation framework. We demonstrate temporally consistent results of artistically stylised game scenes, outperforming state-of-the-art image and video NST methods.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 32 of its 57 references that are in the map. First posted on arXiv 24 Nov 2023.
Builds on
32 of 57 references.
References · 32
- 2014Microsoft COCO: Common Objects in Contextcited by 17,550
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,082
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,687
- 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,005
- 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,490
- 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 782
- 2017Universal Style Transfer via Feature Transformscited by 353
- 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
- 2016A Learned Representation For Artistic Stylecited by 340
- 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
- 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 198
20 more
- 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
- 2021StyTr2: Image Style Transfer with Transformerscited by 187
- 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
- 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 173
- 2016Fast Patch-based Style Transfer of Arbitrary Stylecited by 92
- 2017StyleBank: An Explicit Representation for Neural Image Style Transfercited by 89
- 2017Exploring the structure of a real-time, arbitrary neural artistic stylization networkcited by 89
- 2020Arbitrary Video Style Transfer via Multi-Channel Correlationcited by 84
- 2021Enhancing Photorealism Enhancementcited by 74
- 2017Multi-style Generative Network for Real-time Transfercited by 61
- 2018A Style-Aware Content Loss for Real-time HD Style Transfercited by 58
- 2016Artistic style transfer for videoscited by 54
- 2022CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfercited by 46
- 2017Artistic style transfer for videos and spherical imagescited by 45
- 2019Ultrafast Photorealistic Style Transfer via Neural Architecture Searchcited by 39
- 2019Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfercited by 36
- 2020Manifold Alignment for Semantically Aligned Style Transfercited by 34
- 2018ReCoNet: Real-time Coherent Video Style Transfer Networkcited by 26
- 2022Consistent Style Transfercited by 13
- 2022Depth-aware Neural Style Transfer using Instance Normalizationcited by 9
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.