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Graph."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>This article reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to build gesture-generation systems. Motion generated by all these systems was rendered to video using a standardised visualisation pipeline and evaluated in several large, crowdsourced user studies. Unlike when comparing different research articles, differences in results are here only due to differences between methods, enabling direct comparison between systems. The dataset was based on 18 hours of full-body motion capture, including fingers, of different persons engaging in a dyadic conversation. Ten teams participated in the challenge across two tiers: full-body and upper-body gesticulation. For each tier, we evaluated both the human-likeness of the gesture motion and its appropriateness for the specific speech signal. Our evaluations decouple human-likeness from gesture appropriateness, which has been a difficult problem in the field.<\/jats:p>\n          <jats:p>The evaluation results show some synthetic gesture conditions being rated as significantly more human-like than 3D human motion capture. To the best of our knowledge, this has not been demonstrated before. On the other hand, all synthetic motion is found to be vastly less appropriate for the speech than the original motion-capture recordings. We also find that conventional objective metrics do not correlate well with subjective human-likeness ratings in this large evaluation. The one exception is the Fr\u00e9chet gesture distance (FGD), which achieves a Kendall\u2019s tau rank correlation of around -0.5. Based on the challenge results we formulate numerous recommendations for system building and evaluation.<\/jats:p>","DOI":"10.1145\/3656374","type":"journal-article","created":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T10:01:50Z","timestamp":1714212110000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Evaluating Gesture Generation in a Large-scale Open Challenge: The GENEA Challenge 2022"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9838-8848","authenticated-orcid":false,"given":"Taras","family":"Kucherenko","sequence":"first","affiliation":[{"name":"SEED, Electronic Arts Inc, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7420-7181","authenticated-orcid":false,"given":"Pieter","family":"Wolfert","sequence":"additional","affiliation":[{"name":"Donders Institute for Brain, Cognition &amp; Behaviour, Radboud Universiteit, Nijmegen, Netherlands and IDLab, Ghent University, Gent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4286-3421","authenticated-orcid":false,"given":"Youngwoo","family":"Yoon","sequence":"additional","affiliation":[{"name":"ETRI, Daejeon, Korea (the Republic of)"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3385-4101","authenticated-orcid":false,"given":"Carla","family":"Viegas","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States and Nova University of Lisbon, Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4573-1400","authenticated-orcid":false,"given":"Teodor","family":"Nikolov","sequence":"additional","affiliation":[{"name":"Department of Computing Science, Ume\u00e5 Universitet, Ume\u00e5, Sweden and Motorica AB, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1817-440X","authenticated-orcid":false,"given":"Mihail","family":"Tsakov","sequence":"additional","affiliation":[{"name":"Department of Computing Science, Ume\u00e5 Universitet, Ume\u00e5, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1643-1054","authenticated-orcid":false,"given":"Gustav Eje","family":"Henter","sequence":"additional","affiliation":[{"name":"Division of Speech, Music and Hearing, KTH Royal Institute of Technology, Stockholm, Sweden and Motorica AB, Stockholm, Sweden"}]}],"member":"320","published-online":{"date-parts":[[2024,6,23]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.170"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01991"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.4088599"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13946"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592458"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555435"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/566570.566606"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1121\/1.4919317"},{"key":"e_1_3_3_10_1","first-page":"12449","volume-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS \u201920)","author":"Baevski Alexei","year":"2020","unstructured":"Alexei Baevski , Yuhao Zhou , Abdelrahman Mohamed , and Michael Auli . 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. 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