Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks

Abstract : 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.
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Submitted on : Thursday, September 28, 2017 - 12:32:08 PM
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  • HAL Id : hal-01583706, version 3

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Eric Guérin, Julie Digne, Eric Galin, Adrien Peytavie, Christian Wolf, et al.. Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks. ACM Transactions on Graphics, Association for Computing Machinery, 2017, 36 (6), ⟨https://dl.acm.org/citation.cfm?id=3130804⟩. ⟨hal-01583706v3⟩

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