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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Transactions on Graphics (Proceedings of Siggraph Asia 2017), ACM, 2017, 36, pp.228 - 228. 〈10.1145/3130800.3130804〉
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Soumis le : jeudi 28 septembre 2017 - 12:32:08
Dernière modification le : jeudi 5 octobre 2017 - 01:13:34

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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. Transactions on Graphics (Proceedings of Siggraph Asia 2017), ACM, 2017, 36, pp.228 - 228. 〈10.1145/3130800.3130804〉. 〈hal-01583706v3〉

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