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Existing workflow of modeling, simulation, and rendering closely replicates the physics behind real garments, but is tedious and requires repeating most of the workflow under changes to characters' motion, camera angle, or garment resizing. Although data-driven solutions exist, they either focus on static scenarios or only handle dynamics of tight garments. We present a solution that, at test time, takes in body joint motion to directly produce realistic dynamic garment image sequences. Specifically, given the target joint motion sequence of an avatar, we propose\n            <jats:italic>dynamic neural garments<\/jats:italic>\n            to synthesize plausible dynamic garment appearance from a desired viewpoint. Technically, our solution generates a coarse garment proxy sequence, learns deep dynamic features attached to this template, and neurally renders the features to produce appearance changes such as folds, wrinkles, and silhouettes. We demonstrate generalization behavior to both unseen motion and unseen camera views. Further, our network can be fine-tuned to adopt to new body shape and\/or background images. We demonstrate our method on a wide range of real and synthetic garments. We also provide comparisons against existing neural rendering and image sequence translation approaches, and report clear quantitative and qualitative improvements. Project page: http:\/\/geometry.cs.ucl.ac.uk\/projects\/2021\/DynamicNeuralGarments\/\n          <\/jats:p>","DOI":"10.1145\/3478513.3480497","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:29:20Z","timestamp":1639160960000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Dynamic neural garments"],"prefix":"10.1145","volume":"40","author":[{"given":"Meng","family":"Zhang","sequence":"first","affiliation":[{"name":"University College London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuanfeng Y.","family":"Wang","sequence":"additional","affiliation":[{"name":"Adobe Research, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duygu","family":"Ceylan","sequence":"additional","affiliation":[{"name":"Adobe Research, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niloy J.","family":"Mitra","sequence":"additional","affiliation":[{"name":"University College London and Adobe Research, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13632"},{"key":"e_1_2_2_2_1","volume-title":"Neural point-based graphics. arXiv preprint arXiv:1906.08240","author":"Aliev Kara-Ali","year":"2019","unstructured":"Kara-Ali Aliev , Artem Sevastopolsky , Maria Kolos , Dmitry Ulyanov , and Victor Lempitsky . 2019. 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