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While humans move in three dimensions, the vast majority of human motions are captured using video, requiring 2D-to-3D pose and camera recovery, before existing retargeting approaches may be applied. In this paper, we present a new method for retargeting video-captured motion between different human performers, without the need to explicitly reconstruct 3D poses and\/or camera parameters.<\/jats:p>\n          <jats:p>In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view. Our key idea is to train a deep neural network to decompose temporal sequences of 2D poses into three components: motion, skeleton, and camera view-angle. Having extracted such a representation, we are able to re-combine motion with novel skeletons and camera views, and decode a retargeted temporal sequence, which we compare to a ground truth from a synthetic dataset.<\/jats:p>\n          <jats:p>We demonstrate that our framework can be used to robustly extract human motion from videos, bypassing 3D reconstruction, and outperforming existing retargeting methods, when applied to videos in-the-wild. It also enables additional applications, such as performance cloning, video-driven cartoons, and motion retrieval.<\/jats:p>","DOI":"10.1145\/3306346.3322999","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T19:04:08Z","timestamp":1562958248000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":90,"title":["Learning character-agnostic motion for motion retargeting in 2D"],"prefix":"10.1145","volume":"38","author":[{"given":"Kfir","family":"Aberman","sequence":"first","affiliation":[{"name":"Tel-Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rundi","family":"Wu","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dani","family":"Lischinski","sequence":"additional","affiliation":[{"name":"Shandong University, Hebrew University of Jerusalem"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Tel-Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Deep Video-Based Performance Cloning. arXiv preprint arXiv:1808.06847","author":"Aberman Kfir","year":"2018","unstructured":"Kfir Aberman , Mingyi Shi , Jing Liao , Dani Lischinski , Baoquan Chen , and Daniel Cohen-Or . 2018. 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