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Journal articles
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.
Cited literature [56 references]
https://hal.archives-ouvertes.fr/hal-01583706
Contributor : Christian Wolf <>
Submitted on : Thursday, September 28, 2017 - 12:32:08 PM
Last modification on : Thursday, November 21, 2019 - 1:55:33 AM
Contributor : Christian Wolf <>
Submitted on : Thursday, September 28, 2017 - 12:32:08 PM
Last modification on : Thursday, November 21, 2019 - 1:55:33 AM
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