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Targeting high-quality offline sound synthesis, we seek to resolve animation-driven sound radiation with near-field scattering and diffraction effects. The core of our approach is a sharp-interface finite-difference time-domain (FDTD) wavesolver, with a series of supporting algorithms to handle rapidly deforming and vibrating embedded interfaces arising in physics-based animation sound. Once the solver rasterizes these interfaces, it must evaluate acceleration boundary conditions (BCs) that involve model-and phenomena-specific computations. We introduce\n            <jats:italic>acoustic shaders<\/jats:italic>\n            as a mechanism to abstract away these complexities, and describe a variety of implementations for computer animation: near-rigid objects with ringing and acceleration noise, deformable (finite element) models such as thin shells, bubble-based water, and virtual characters. Since time-domain wave synthesis is expensive, we only simulate pressure waves in a small region about each sound source, then estimate a far-field pressure signal. To further improve scalability beyond multi-threading, we propose a fully\n            <jats:italic>time-parallel sound synthesis<\/jats:italic>\n            method that is demonstrated on commodity cloud computing resources. In addition to presenting results for multiple animation phenomena (water, rigid, shells, kinematic deformers, etc.) we also propose 3D automatic dialogue replacement (3DADR) for virtual characters so that pre-recorded dialogue can include character movement, and near-field shadowing and scattering sound effects.\n          <\/jats:p>","DOI":"10.1145\/3197517.3201318","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T15:56:23Z","timestamp":1533052583000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Toward wave-based sound synthesis for computer animation"],"prefix":"10.1145","volume":"37","author":[{"given":"Jui-Hsien","family":"Wang","sequence":"first","affiliation":[{"name":"Stanford University"}]},{"given":"Ante","family":"Qu","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Timothy R.","family":"Langlois","sequence":"additional","affiliation":[{"name":"Adobe Research"}]},{"given":"Doug L.","family":"James","sequence":"additional","affiliation":[{"name":"Stanford University"}]}],"member":"320","published-online":{"date-parts":[[2018,7,30]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1080\/10867651.2001.10487535"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2767001"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185598"},{"key":"e_1_2_2_4_1","unstructured":"Avid Technology. 2018. 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