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Date: Tuesday, November 19th
Time: 11:00am - 12:45pm
Venue: Plaza Meeting Room P1
Session Chair(s): Paul Guerrero, Adobe Research, University College London

Cubic Stylization

Abstract: We present a 3D stylization algorithm that can turn an input shape into the style of a cube while maintaining the content of the original shape. The key insight is that cubic style sculptures can be captured by the as-rigid-as-possible energy with an L1-regularization on rotated surface normals. Minimizing this energy naturally leads to a detail-preserving, cubic geometry. Our optimization can be solved efficiently without any mesh surgery. Our method serves as a non-realistic modeling tool where one can incorporate many artistic controls to create stylized geometries.

Authors/Presenter(s): Hsueh-Ti Derek Liu, University of Toronto, Canada
Alec Jacobson, University of Toronto, Canada

LOGAN: Unpaired Shape Transform in Latent Overcomplete Space

Abstract: We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and chairs, while there is neither a pairing between shapes from the domains as supervision nor any point-wise correspondence between any shapes. Once trained, LOGAN takes a shape from one domain and transforms it into the other. Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes concatenate multi-scale shape features, resulting in an overcomplete representation. The translator is based on a generative adversarial network (GAN), operating in the latent space, where an adversarial loss enforces cross-domain translation while a feature preservation loss ensures that the right shape features are preserved for a natural shape transform. We conduct ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. We show that LOGAN is able to learn what shape features to preserve during shape translation, either local or non-local, whether content or style, depending solely on the input domains for training.

Authors/Presenter(s): Kangxue Yin, Simon Fraser University, Canada
Zhiqin Chen, Simon Fraser University, Canada
Hui Huang, Shenzhen University, China
Daniel Cohen-Or, Tel Aviv University, Israel
Hao (Richard) Zhang, Simon Fraser University, Canada

Orometry-based terrain analysis and synthesis

Abstract: Mountainous digital terrains are an important element of many virtual environments and find application in games, film, simulation and training. Unfortunately, while existing synthesis methods produce locally plausible results they often fail to respect global structure. This is exacerbated by a dearth of automated metrics for assessing terrain properties at a macro level. We address these issues by building on techniques from orometry, a field that involves the measurement of mountains and other relief features. First, we construct a sparse metric computed on the peaks and saddles of a mountain range and show that, when used for classification, this is capable of robustly distinguishing between different mountain ranges. Second, we present a synthesis method that takes a coarse elevation map as input and builds a graph of peaks and saddles respecting a given orometric distribution. This is then expanded into a fully continuous elevation function by deriving a consistent river network and shaping the valley slopes. In terms of authoring, users provide various control maps and are also able to edit, reposition, insert and remove terrain features all while retaining the characteristics of a selected mountain range. The result is a terrain analysis and synthesis method that considers and incorporates orometric properties, and is, on the basis of our user study, more visually plausible than existing terrain generation methods.

Authors/Presenter(s): Oscar Argudo, Univ Lyon, Université Lyon 1, CNRS, LIRIS, France
Eric Galin, Univ Lyon, Université Lyon 1, CNRS, LIRIS, France
Adrien Peytavie, Univ Lyon, Université Lyon 1, CNRS, LIRIS, France
Axel Paris, Univ Lyon, INSA-Lyon, CNRS, LIRIS, France
James Gain, University of Cape Town, South Africa
Eric Guérin, Univ Lyon, INSA-Lyon, CNRS, LIRIS, France

Multi-Theme Generative Adversarial Terrain Amplification

Abstract: Achieving highly detailed terrain models spanning vast areas is crucial to modern computer graphics. The pipeline for obtaining such terrains is via amplification of a low-resolution terrain to refine the details given a desired theme, which is a time-consuming and labor-intensive process. Recently, data-driven methods, such as the sparse construction tree, have provided a promising direction to equip the artist with better control over the theme. These methods learn to amplify terrain details by using an exemplar of high-resolution detailed terrains to transfer the theme. In this paper, we propose Generative Adversarial Terrain Amplification (GATA) that achieves better local/global coherence compared to the existing data-driven methods while providing even more ways to control the theme. GATA is comprised of two key ingredients. The first one is a novel embedding of themes into vectors of real numbers to achieve a single tool for multi-theme amplification. The theme component can leverage existing LIDAR data to generate similar terrain features. It can also generate new fictional themes by tuning the embedding vector or even encoding a new example terrain into an embedding. The second one is an adversarially trained model that, conditioned on an embedding and a low-resolution terrain, generates a high-resolution terrain adhering to the desired theme. The proposed integral approach reduces the need for unnecessary manual adjustments, can speed up the development, and brings the model quality to a new level. Our implementation of the proposed method has proved successful in large-scale terrain authoring for an open-world game.

Authors/Presenter(s): Yiwei Zhao, Electronic Arts, United States of America
Han Liu, Electronic Arts, United States of America
Igor Borovikov, Electronic Arts, United States of America
Ahmad Beirami, Electronic Arts, United States of America
Maziar Sanjabi, Electronic Arts, United States of America
Kazi Zaman, Electronic Arts, United States of America

Terrain Amplification with Implicit 3D Features

Abstract: While three-dimensional landforms, such as arches and overhangs, occupy a relatively small proportion of most computer generated landscapes, they are distinctive and dramatic and have an outsize visual impact. Unfortunately, the dominant heightfield representation of terrain precludes such features, and existing in-memory volumetric structures are too memory intensive to handle larger scenes. In this paper, we present a novel memory-optimized paradigm for representing and generating volumetric terrain based on implicit surfaces. We encode feature shapes and terrain geology using construction trees that arrange and combine implicit primitives. The landform primitives themselves are positioned using Poisson sampling, built using open shape grammars guided by stratified erosion and invasion percolation processes, and, finally, queried during polygonization. Users can also interactively author landforms using high-level modeling tools to create or edit the underlying construction trees, with support for iterative cycles of editing and simulation. We demonstrate that our framework is capable of importing existing large-scale heightfield terrains and amplifying them with such diverse structures as slot canyons, sea arches, stratified cliffs, fields of hoodoos, and complex karst cave networks.

Authors/Presenter(s): Axel Paris, Univ Lyon, CNRS, LIRIS, France
Eric Galin, Univ Lyon, CNRS, LIRIS, France
Adrien Peytavie, Univ Lyon, CNRS, LIRIS, France
Eric Guérin, Univ Lyon, INSA, CNRS, LIRIS, France
James Gain, University of Cape Town, South Africa