Deep Generative Framework for Interactive 3D Terrain Authoring and Manipulation

Automated generation and (user) authoring of realistic virtual terrain is most sought for by the multimedia applications like VR models and gaming. The most common representation adopted for terrain is Digital Elevation Model (DEM). In this paper, we propose a novel realistic terrain authoring framework powered by a combination of VAE and generative conditional GAN model. Our framework is an example-based method that attempts to overcome the limitations of existing methods by learning a latent space from a real-world terrain dataset. This latent space allows us to generate multiple variants of terrain from a single input as well as interpolate between terrains while keeping the generated terrains close to real-world data distribution. We also developed an interactive tool that lets the user generate diverse terrains with minimal inputs. We perform a thorough qualitative and quantitative analysis and provide a comparison with other SOTA methods.