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Graph."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>\n            We present GenMM, a generative model that \"mines\" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known\n            <jats:italic>Motion Matching<\/jats:italic>\n            method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.\n          <\/jats:p>","DOI":"10.1145\/3592395","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T14:29:21Z","timestamp":1690381761000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Example-based Motion Synthesis via Generative Motion Matching"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4500-4905","authenticated-orcid":false,"given":"Weiyu","family":"Li","sequence":"first","affiliation":[{"name":"Shandong University, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0158-9469","authenticated-orcid":false,"given":"Xuelin","family":"Chen","sequence":"additional","affiliation":[{"name":"Tencent AI Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9309-9967","authenticated-orcid":false,"given":"Peizhuo","family":"Li","sequence":"additional","affiliation":[{"name":"ETH Zurich, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-3974","authenticated-orcid":false,"given":"Olga","family":"Sorkine-Hornung","sequence":"additional","affiliation":[{"name":"ETH Zurich, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4702-036X","authenticated-orcid":false,"given":"Baoquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Adobe Systems Inc. 2022. 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