Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks

Eric Guérin (1) , Julie Digne (1) , Eric Galin (1) , Adrien Peytavie (1) , Christian Wolf (2) , Bedrich Benes (3) , Benoît Martinez (4)

1 GeoMod - Modélisation Géométrique, Géométrie Algorithmique, Fractales
2 imagine - Extraction de Caractéristiques et Identification
3 Department of Computer Science [Purdue]
4 Ubisoft

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.

Mots clés: Procedural modeling; Terrain generation; Deep Learning

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]

Eric Guérin, Julie Digne, Eric Galin, Adrien Peytavie, Christian Wolf, et al.. Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks. 2017. ⟨hal-01583706v2⟩