Publications

Publications

You will find on this page all scientific publications related to HDWorlds project.

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2018

• Procedural Cloudscapes
  A. Webanck, Y. Cortial, E. Guérin, E. Galin
  Computer Graphics Forum, 37(2), Eurographics, 2018
  
  
  

  Our procedural cloudscape model allows to represent different types of clouds.

  Abstract: We present a phenomenological approach for modeling and animating cloudscapes. We propose a compact procedural model for representing the different types of cloud over a range of altitudes. We define primitive-based field functions that allow the user to control and author the cloud cover over large distances easily. Our approach allows us to animate cloudscapes by morphing: instead of simulating the evolution of clouds using a physically-based simulation, we compute the movement of clouds using key-frame interpolation and tackle the morphing problem as an Optimal Transport problem. The trajectories of the cloud cover primitives are generated by solving an Anisotropic Shortest Path problem with a cost function that takes into account the elevation of the terrain and the parameters of the wind field.
  [Get the paper here ]

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2017

• Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks
  E. Guérin, J. Digne, E. Galin, A. Peytavie, C. Wolf, B. Benes, B. Martinez
  Transactions on Graphics (Proceedings of Siggraph Asia), 2017
  
  
  

  Our interactive terrain modeling framework allows the user to quickly, easily, and intuitively author realistic terrain models by using sketches of crest lines, rivers, or iso contours. Our method consists of a training step and an interactive sketching step. During the training step, we analyze a large number of terrains and extract geometric features that will serve as sketching elements. The sketches are fed to a set of cGANs which learn various generative processes. During the authoring step, the pre-trained networks are used to synthesize a terrain model that matches the input sketch and the incremental edits.

  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.
  [Get the paper here ]


• G. Guingo, B. Sauvage, J-M. Dischler, M-P. Cani, Bi-Layer textures: a Model for Synthesis and Deformation of Composite Textures, Eurographics Symposium on Rendering, 2017.


• Authoring Landscapes by Combining Ecosystem and Terrain Erosion Simulation
  G. Cordonnier, E. Galin, J. Gain, B. Benes, E. Guérin, A. Peytavie, M.P. Cani
  SIGGRAPH, 2017
  
  
  

  Our framework combines layered terrain and vegetation data and supports their interlinked simulation, which can be driven by users editing layers or triggering natural events. (1) The user first provides a bare-earth digital elevation map for time step t0 and our framework simulates interleaved erosion and plant growth, up to t0+215 years. (2) In the next time step at t0+210 years, a landslide creates boulders that destroy vegetation. One year later, the designer triggers a fire in the valley, which spreads to consume part of the forest. (3) After four more years at t0+215, the remaining trees have continued growing, new saplings have germinated and the humus layer is beginning to regenerate. The white loops indicate affected areas.

  Abstract: We introduce a novel framework for interactive landscape authoring that supports bi-directional feedback between erosion and vegetation simulation. Vegetation and terrain erosion have strong mutual impact and their interplay influences the overall realism of virtual scenes. Despite their importance, these complex interactions have been neglected in computer graphics. Our framework overcomes this by simulating the effect of a variety of geomorphological agents and the mutual interaction between different material and vegetation layers, including rock, sand, humus, grass, shrubs, and trees. Users are able to exploit these interactions with an authoring interface that consistently shapes the terrain and populates it with details. Our method, validated through side-by-side comparison with real terrains, can be used not only to generate realistic static landscapes, but also to follow the temporal evolution of a landscape over a few centuries.
  [Get the paper here ]


• Coherent multi-layer landscape synthesis
  O. Argudo, C. Andujar, A. Chica, E. Guérin, J. Digne, A. Peytavie, E. Galin
  The Visual Computer, 2017
  
  
  

  Overview of our coherent multi-layer landscape synthesis: given a set of input exemplars, our method automatically creates a high-resolution consistent and coherent multi-layer terrain model from a low-resolution elevation model by matching input patches with the nearest dictionary atoms.

  Abstract: We present an efficient method for generating coherent multi-layer landscapes. We use a dictionary built from exemplars to synthesize high-resolution fully featured terrains from input low-resolution elevation data. Our example-based method consists in analyzing real-world terrain examples and learning the procedural rules directly from these inputs. We take into account not only the elevation of the terrain, but also additional layers such as the slope, orientation, drainage area, the density and distribution of vegetation, and the soil type. By increasing the variety of terrain exemplars, our method allows the user to synthesize and control different types of landscapes and biomes, such as temperate or rain forests, arid deserts and mountains.
  [Get the paper here ]