{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:30:41Z","timestamp":1723015841282},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>We aim to automatically generate human motion sequence from a single input person image, with some specific action label. To this end, we propose a cross-space human motion video generation network which features two paths: a forward path that first samples\/generates a sequence of low dimensional motion vectors based on Gaussian Process (GP), which is paired with the input person image to form a moving human figure sequence; and a backward path based on the predicted human images to re-extract the corresponding latent motion representations. As lack of supervision, the reconstructed latent motion representations are expected to be as close as possible to the GP sampled ones, thus yielding a cyclic objective function for cross-space (i.e., motion and appearance) mutual constrained generation. We further propose an alternative sampling\/generation algorithm with respect to constraints from both spaces. Extensive experimental results show that the proposed framework successfully generates novel human motion sequences with reasonable visual quality.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/105","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"757-763","source":"Crossref","is-referenced-by-count":6,"title":["Human Motion Generation via Cross-Space Constrained Sampling"],"prefix":"10.24963","author":[{"given":"Zhongyue","family":"Huang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwei","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingbing","family":"Ni","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:53Z","timestamp":1530755393000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/105"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/105","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}