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The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions. We also propose a particle-based approach to interpolate between liquids generated from varying simulation discretizations using a state-of-the-art bidirectional optical flow solver method for fluids in addition with a novel key-event topological alignment constraint. In conjunction with the neighborhood contributions, our loss formulation allows the inference model throughout epochs to reward important differences in regard to significant gaps in simulation discretizations. Even when applied in an untested simulation setup, our approach is able to generate plausible high-resolution details. Using this interpolation approach and the predicted displacements, our approach combines the input liquid properties with the predicted motion to infer semi-Lagrangian advection. We furthermore showcase how the proposed interpolation approach can facilitate generating large simulation datasets with a subset of initial condition parameters.<\/jats:p>","DOI":"10.1145\/3480147","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T04:43:36Z","timestamp":1632804216000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Neural UpFlow"],"prefix":"10.1145","volume":"4","author":[{"given":"Bruno","family":"Roy","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al and Autodesk, Canada"}]},{"given":"Pierre","family":"Poulin","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al, Canada"}]},{"given":"Eric","family":"Paquette","sequence":"additional","affiliation":[{"name":"\u00c9cole de technologie sup\u00e9rieure, Canada"}]}],"member":"320","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073625"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392462"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276437"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2012.87"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461982"},{"volume-title":"ACM SIGGRAPH\/Eurographics Symposium on Computer Animation (SCA). 209--217","year":"2007","author":"Becker Markus","key":"e_1_2_2_6_1"},{"key":"e_1_2_2_7_1","article-title":"Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder","volume":"36","author":"Alla Chaitanya Chakravarty R","year":"2017","journal-title":"ACM Trans. on Graphics (TOG)"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2015.2449303"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073643"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3203197"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12825"},{"volume-title":"Proc. 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