Research on realistic effect generation algorithm of rendering images based on GAN

Runmin Gan; Hu Su

doi:10.1117/12.2680499

27 June 2023 Research on realistic effect generation algorithm of rendering images based on GAN

Runmin Gan, Hu Su

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127050S (2023) https://doi.org/10.1117/12.2680499
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Saving algorithmic overhead for realistic effect generation is always a hot topic in graphics research. The algorithm in this paper applies the method of the Generative Adversarial Network (GAN) to the study of realistic computer graphics, proposing a new solution to reduce the algorithm overhead. We adopt conditional GAN and add an additional module before the generator to provide the lighting information of the current scene. This extra module represents the illuminating direction with grayscale gradient maps of different angles helping the network get lighting information through image input. In addition, we optimize the loss function by adding the L1 loss and the feature perception loss to improve the generative effect of our network. In the feature perception loss, we use a pre-trained VGG network to calculate the detail feature gap between images, to help the model generate images with better light and shadow effects. Our algorithm can add realistic effects to the existing coarse-rendered image according to the lighting conditions and obtain the corresponding fine-rendered image in single light scenes. The extensive experimental results show that our algorithm has a good post-processing effect on realistic rendering, and the time overhead of the algorithm is independent of the complexity of the scene. From the robustness test, we can know that our network also has a good generalization ability.

Citation Download Citation

Runmin Gan and Hu Su "Research on realistic effect generation algorithm of rendering images based on GAN", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127050S (27 June 2023); https://doi.org/10.1117/12.2680499

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KEYWORDS

Shadows

Light sources and illumination

Data modeling

Visualization

Convolution

Network architectures

Deep learning

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