Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks

doi:10.1145/3130800.3130804

Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks

2017-11-20

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DOI：10.1145/3130800.3130804

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Authoring virtual terrains presents a challenge and there is a strong need for authoring tools able to create realistic terrains with simple user-inputs and with high user control. We propose an example-based authoring pipeline that uses a set of terrain synthesizers dedicated to specific tasks. Each terrain synthesizer is a Conditional Generative Adversarial Network trained by using real-world terrains and their sketched counterparts. The training sets are built automatically with a view that the terrain synthesizers learn the generation from features that are easy to sketch. During the authoring process, the artist first creates a rough sketch of the main terrain features, such as rivers, valleys and ridges, and the algorithm automatically synthesizes a terrain corresponding to the sketch using the learned features of the training samples. Moreover, an erosion synthesizer can also generate terrain evolution by erosion at a very low computational cost. Our framework allows for an easy terrain authoring and provides a high level of realism for a minimum sketch cost. We show various examples of terrain synthesis created by experienced as well as inexperienced users who are able to design a vast variety of complex terrains in a very short time.

Keywords:

Procedural modeling

Terrain generation

Deep Learning

Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks

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