{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:10:40Z","timestamp":1784268640615,"version":"3.55.0"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Comput. Graph. Interact. Tech."],"published-print":{"date-parts":[[2022,5,4]]},"abstract":"<jats:p>Controlling the manner in which a character moves in a real-time animation system is a challenging task with useful applications. Existing style transfer systems require access to a reference content motion clip, however, in real-time systems the future motion content is unknown and liable to change with user input. In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases. An additional style modulation network uses feature-wise transformations to modulate style in real-time. To evaluate our method, we create and release a new style modelling dataset, 100STYLE, containing over 4 million frames of stylised locomotion data in 100 different styles that present a number of challenges for existing systems. To model these styles, we extend the local phase calculation with a contact-free formulation. In comparison to other methods for real-time style modelling, we show our system is more robust and efficient in its style representation while improving motion quality.<\/jats:p>","DOI":"10.1145\/3522618","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T17:31:08Z","timestamp":1651685468000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":70,"title":["Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases"],"prefix":"10.1145","volume":"5","author":[{"given":"Ian","family":"Mason","sequence":"first","affiliation":[{"name":"University of Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Starke","sequence":"additional","affiliation":[{"name":"University of Edinburgh and Electronic Arts, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taku","family":"Komura","sequence":"additional","affiliation":[{"name":"University of Edinburgh, United Kingdom and University of Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392462"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392469"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00296"},{"key":"e_1_2_2_4_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba , Jamie Ryan Kiros, and Geoffrey E Hinton . 2016 . Layer normalization. arXiv preprint arXiv:1607.06450 (2016). Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016)."},{"key":"e_1_2_2_5_1","volume-title":"Customizing sequence generation with multi-task dynamical systems. arXiv preprint arXiv:1910.05026","author":"Bird Alex","year":"2019","unstructured":"Alex Bird and Christopher K I Williams . 2019. Customizing sequence generation with multi-task dynamical systems. arXiv preprint arXiv:1910.05026 ( 2019 ). Alex Bird and Christopher K I Williams. 2019. Customizing sequence generation with multi-task dynamical systems. arXiv preprint arXiv:1910.05026 (2019)."},{"key":"e_1_2_2_6_1","volume-title":"Deep representation learning for human motion prediction and classification. arXiv preprint arXiv:1702.07486","author":"B\u00fctepage Judith","year":"2017","unstructured":"Judith B\u00fctepage , Michael Black , Danica Kragic , and Hedvig Kjellstr\u00f6m . 2017. Deep representation learning for human motion prediction and classification. arXiv preprint arXiv:1702.07486 ( 2017 ). Judith B\u00fctepage, Michael Black, Danica Kragic, and Hedvig Kjellstr\u00f6m. 2017. Deep representation learning for human motion prediction and classification. arXiv preprint arXiv:1702.07486 (2017)."},{"key":"e_1_2_2_7_1","unstructured":"Simon Clavet. 2016. Motion matching and the road to next-gen animation. In GDC.  Simon Clavet. 2016. Motion matching and the road to next-gen animation. In GDC."},{"key":"e_1_2_2_8_1","volume-title":"Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289","author":"Clevert Djork-Arn\u00e9","year":"2015","unstructured":"Djork-Arn\u00e9 Clevert , Thomas Unterthiner , and Sepp Hochreiter . 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 ( 2015 ). Djork-Arn\u00e9 Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)."},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3424636.3426909"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359566.3360083"},{"key":"e_1_2_2_11_1","volume-title":"Feature-wise transformations. Distill","author":"Dumoulin Vincent","year":"2018","unstructured":"Vincent Dumoulin , Ethan Perez , Nathan Schucher , Florian Strub , Harm de Vries , Aaron Courville , and Yoshua Bengio . 2018. Feature-wise transformations. Distill ( 2018 ). https:\/\/doi.org\/10.23915\/distill.00011 10.23915\/distill.00011 Vincent Dumoulin, Ethan Perez, Nathan Schucher, Florian Strub, Harm de Vries, Aaron Courville, and Yoshua Bengio. 2018. Feature-wise transformations. Distill (2018). https:\/\/doi.org\/10.23915\/distill.00011"},{"key":"e_1_2_2_12_1","unstructured":"Vincent Dumoulin Jonathon Shlens and Manjunath Kudlur. 2017. A learned representation for artistic style. In ICLR.  Vincent Dumoulin Jonathon Shlens and Manjunath Kudlur. 2017. A learned representation for artistic style. In ICLR."},{"key":"e_1_2_2_13_1","volume-title":"Source-free adaptation to measurement shift via bottom-up feature restoration. arXiv preprint arXiv:2107.05446","author":"Eastwood Cian","year":"2021","unstructured":"Cian Eastwood , Ian Mason , Christopher K.I. Williams , and Bernhard Sch\u00f6lkopf . 2021. Source-free adaptation to measurement shift via bottom-up feature restoration. arXiv preprint arXiv:2107.05446 ( 2021 ). Cian Eastwood, Ian Mason, Christopher K.I. Williams, and Bernhard Sch\u00f6lkopf. 2021. Source-free adaptation to measurement shift via bottom-up feature restoration. arXiv preprint arXiv:2107.05446 (2021)."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/2919332.2919834"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3478513.3480527"},{"key":"e_1_2_2_16_1","volume-title":"A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576","author":"Gatys Leon A","year":"2015","unstructured":"Leon A Gatys , Alexander S Ecker , and Matthias Bethge . 2015. A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 ( 2015 ). Leon A Gatys, Alexander S Ecker, and Matthias Bethge. 2015. A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)."},{"key":"e_1_2_2_18_1","volume-title":"World models. arXiv preprint arXiv:1803.10122","author":"Ha David","year":"2018","unstructured":"David Ha and J\u00fcrgen Schmidhuber . 2018. World models. arXiv preprint arXiv:1803.10122 ( 2018 ). David Ha and J\u00fcrgen Schmidhuber. 2018. World models. arXiv preprint arXiv:1803.10122 (2018)."},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417836"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCG.2017.3271464"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392440"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073663"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925975"},{"key":"e_1_2_2_24_1","volume-title":"Source free domain adaptation with image translation. arXiv preprint arXiv:2008.07514","author":"Hou Yunzhong","year":"2020","unstructured":"Yunzhong Hou and Liang Zheng . 2020. Source free domain adaptation with image translation. arXiv preprint arXiv:2008.07514 ( 2020 ). Yunzhong Hou and Liang Zheng. 2020. Source free domain adaptation with image translation. arXiv preprint arXiv:2008.07514 (2020)."},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1186822.1073315"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"e_1_2_2_27_1","volume-title":"Multimodal unsupervised image-to-image translation. arXiv preprint arXiv:1804.04732","author":"Huang Xun","year":"2018","unstructured":"Xun Huang , Ming-Yu Liu , Serge Belongie , and Jan Kautz . 2018. Multimodal unsupervised image-to-image translation. arXiv preprint arXiv:1804.04732 ( 2018 ). Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz. 2018. Multimodal unsupervised image-to-image translation. arXiv preprint arXiv:1804.04732 (2018)."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1477926.1477927"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.248"},{"key":"e_1_2_2_30_1","volume-title":"Test sample accuracy scales with training sample density in neural networks. arXiv preprint arXiv:2106.08365","author":"Ji Xu","year":"2021","unstructured":"Xu Ji , Razvan Pascanu , Devon Hjelm , Balaji Lakshminarayanan , and Andrea Vedaldi . 2021. Test sample accuracy scales with training sample density in neural networks. arXiv preprint arXiv:2106.08365 ( 2021 ). Xu Ji, Razvan Pascanu, Devon Hjelm, Balaji Lakshminarayanan, and Andrea Vedaldi. 2021. Test sample accuracy scales with training sample density in neural networks. arXiv preprint arXiv:2106.08365 (2021)."},{"key":"e_1_2_2_31_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566605"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275016"},{"key":"e_1_2_2_34_1","unstructured":"Zimo Li Yi Zhou Shuangjiu Xiao Chong He and Hao Li. 2018. Auto-conditioned LSTM network for extended complex human motion synthesis. In ICLR.  Zimo Li Yi Zhou Shuangjiu Xiao Chong He and Hao Li. 2018. Auto-conditioned LSTM network for extended complex human motion synthesis. In ICLR."},{"key":"e_1_2_2_35_1","volume-title":"Character controllers using motion VAEs. 39, 4","author":"Ling Hung Yu","year":"2020","unstructured":"Hung Yu Ling , Fabio Zinno , George Cheng , and Michiel van de Panne . 2020. Character controllers using motion VAEs. 39, 4 ( 2020 ). Hung Yu Ling, Fabio Zinno, George Cheng, and Michiel van de Panne. 2020. Character controllers using motion VAEs. 39, 4 (2020)."},{"key":"e_1_2_2_36_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of Machine Learning Research 9 , Nov (2008), 2579 -- 2605 . Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579--2605.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.497"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13555"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1186822.1073313"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3480145"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201311"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073602"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"e_1_2_2_44_1","unstructured":"Sylvestre-Alvise Rebuffi Hakan Bilen and Andrea Vedaldi. 2017. Learning multiple visual domains with residual adapters. In Advances in Neural Information Processing Systems. 506--516.  Sylvestre-Alvise Rebuffi Hakan Bilen and Andrea Vedaldi. 2017. Learning multiple visual domains with residual adapters. In Advances in Neural Information Processing Systems. 506--516."},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00847"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/38.708559"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015754"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340254"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356505"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392450"},{"key":"e_1_2_2_52_1","volume-title":"Dataset Shift in Machine Learning","author":"Storkey Amos J","unstructured":"Amos J Storkey . 2009. When training and test sets are different: characterising learning transfer . In Dataset Shift in Machine Learning . MIT Press , 3--28. Amos J Storkey. 2009. When training and test sets are different: characterising learning transfer. In Dataset Shift in Machine Learning. MIT Press, 3--28."},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553505"},{"key":"e_1_2_2_54_1","unstructured":"Julius von K\u00fcgelgen Yash Sharma Luigi Gresele Wieland Brendel Bernhard Sch\u00f6lkopf Michel Besserve and Francesco Locatello. 2021. Self-supervised learning with data augmentations provably isolates content from style. In Advances in Neural Information Processing Systems.  Julius von K\u00fcgelgen Yash Sharma Luigi Gresele Wieland Brendel Bernhard Sch\u00f6lkopf Michel Besserve and Francesco Locatello. 2021. Self-supervised learning with data augmentations provably isolates content from style. In Advances in Neural Information Processing Systems."},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141991"},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01340"},{"key":"e_1_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766999"},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276509"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201397"},{"key":"e_1_2_2_60_1","first-page":"137","article-title":"Spectral style transfer for human motion between independent actions","volume":"35","author":"Ersin Yumer M","year":"2016","unstructured":"M Ersin Yumer and Niloy J Mitra . 2016 . Spectral style transfer for human motion between independent actions . ACM Transactions on Graphics (TOG) 35 , 4 (2016), 137 . M Ersin Yumer and Niloy J Mitra. 2016. Spectral style transfer for human motion between independent actions. ACM Transactions on Graphics (TOG) 35, 4 (2016), 137.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201366"}],"container-title":["Proceedings of the ACM on Computer Graphics and Interactive Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522618","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3522618","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:34Z","timestamp":1750183774000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,5,4]]}},"alternative-id":["10.1145\/3522618"],"URL":"https:\/\/doi.org\/10.1145\/3522618","relation":{},"ISSN":["2577-6193"],"issn-type":[{"value":"2577-6193","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,4]]},"assertion":[{"value":"2022-05-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}