Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks - Archive ouverte HAL Access content directly
Journal Articles ACM Transactions on Graphics Year : 2017

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.
Fichier principal
Vignette du fichier
tog.pdf (32.06 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01583706 , version 1 (07-09-2017)
hal-01583706 , version 2 (08-09-2017)
hal-01583706 , version 3 (28-09-2017)

Identifiers

  • HAL Id : hal-01583706 , version 3

Cite

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, 2017, 36 (6). ⟨hal-01583706v3⟩
2733 View
18559 Download

Share

Gmail Facebook X LinkedIn More