Computer Science > Graphics
arXiv:2306.04629 (cs)
[Submitted on 24 May 2023]
Title:Generative Adversarial Shaders for Real-Time Realism Enhancement
View a PDF of the paper titled Generative Adversarial Shaders for Real-Time Realism Enhancement, by Arturo Salmi and 2 other authors
View PDFAbstract:Application of realism enhancement methods, particularly in real-time and resource-constrained settings, has been frustrated by the expense of existing methods. These achieve high quality results only at the cost of long runtimes and high bandwidth, memory, and power requirements. We present an efficient alternative: a high-performance, generative shader-based approach that adapts machine learning techniques to real-time applications, even in resource-constrained settings such as embedded and mobile GPUs. The proposed learnable shader pipeline comprises differentiable functions that can be trained in an end-to-end manner using an adversarial objective, allowing for faithful reproduction of the appearance of a target image set without manual tuning. The shader pipeline is optimized for highly efficient execution on the target device, providing temporally stable, faster-than-real time results with quality competitive with many neural network-based methods.
| Comments: | 12 pages, 9 figures, 2 tables |
| Subjects: | Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| ACM classes: | I.2; I.3; I.4 |
| Cite as: | arXiv:2306.04629 [cs.GR] |
| (orarXiv:2306.04629v1 [cs.GR] for this version) | |
| https://doi.org/10.48550/arXiv.2306.04629 arXiv-issued DOI via DataCite |
Submission history
From: Szabolcs Cséfalvay [view email][v1] Wed, 24 May 2023 15:42:38 UTC (44,413 KB)
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View a PDF of the paper titled Generative Adversarial Shaders for Real-Time Realism Enhancement, by Arturo Salmi and 2 other authors
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