Multi-scale terrain texturing using generative adversarial networks

J Klein, S Hartmann, M Weinmann… - … Conference on Image …, 2017 - ieeexplore.ieee.org
2017 International Conference on Image and Vision Computing New …, 2017ieeexplore.ieee.org
We propose a novel, automatic generation process for detail maps that allows the reduction
of tiling artifacts in real-time terrain rendering. This is achieved by training a generative
adversarial network (GAN) with a single input texture and subsequently using it to
synthesize a huge texture spanning the whole terrain. The low-frequency components of the
GAN output are extracted, down-scaled and combined with the high-frequency components
of the input texture during rendering. This results in a terrain texture that is both highly …
We propose a novel, automatic generation process for detail maps that allows the reduction of tiling artifacts in real-time terrain rendering. This is achieved by training a generative adversarial network (GAN) with a single input texture and subsequently using it to synthesize a huge texture spanning the whole terrain. The low-frequency components of the GAN output are extracted, down-scaled and combined with the high-frequency components of the input texture during rendering. This results in a terrain texture that is both highly detailed and non-repetitive, which eliminates the tiling artifacts without decreasing overall image quality. The rendering is efficient regarding both memory consumption and computational costs. Furthermore, it is orthogonal to other techniques for terrain texture improvements such as texture splatting and can directly be combined with them.
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