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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.<\/jats:p>\n          <jats:p>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. Thefi rst 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 newfi ctional 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.<\/jats:p>","DOI":"10.1145\/3355089.3356553","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T20:27:58Z","timestamp":1573244878000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-theme generative adversarial terrain amplification"],"prefix":"10.1145","volume":"38","author":[{"given":"Yiwei","family":"Zhao","sequence":"first","affiliation":[{"name":"EA Digital Platform - Data &amp; AI, Electronic Arts"}]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"EA Digital Platform - Data &amp; AI"}]},{"given":"Igor","family":"Borovikov","sequence":"additional","affiliation":[{"name":"EA Digital Platform - Data &amp; AI"}]},{"given":"Ahmad","family":"Beirami","sequence":"additional","affiliation":[{"name":"EA Digital Platform - Data &amp; AI"}]},{"given":"Maziar","family":"Sanjabi","sequence":"additional","affiliation":[{"name":"EA Digital Platform - Data &amp; AI"}]},{"given":"Kazi","family":"Zaman","sequence":"additional","affiliation":[{"name":"EA Digital Platform - Data &amp; AI"}]}],"member":"320","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Tensorflow: A system for large-scale machine learning. 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