Motion In-betweening via Recursive Keyframe Prediction

Motion In-betweening via Recursive Keyframe Prediction | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Computer Animation and Virtual Worlds This is a preprint and has not been peer reviewed. Data may be preliminary. 28 April 2025 V1 Latest version Share on Motion In-betweening via Recursive Keyframe Prediction Authors : Rui Zeng 0000-0001-9688-5875 , Ju Dai 0000-0002-9397-8539 [email protected] , Junxuan Bai 0000-0002-7941-0584 , and Junjun Pan Authors Info & Affiliations https://doi.org/10.22541/au.174582061.11145395/v1 Published Computer Animation and Virtual Worlds Version of record Peer review timeline 389 views 253 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Motion in-betweening is a flexible and efficient technique for generating 3-dimensional animations. In this paper, we propose a keyframe-driven method that effectively addresses the pose ambiguity issue and achieves robust in-betweening performance. We introduce a keyframe-driven synthesis framework. At each recursion, the key poses at both ends keep predicting the new one at the midpoint. The recursive breakdown reduces motion ambiguities by simplifying the in-betweening sequence as the integration of short clips. The hybrid positional encoding scales the hidden states to adapt to long-and-short-term dependencies. Additionally, we employ a temporal refinement network to capture the local motion relationships, thereby enhancing the consistency of the predicted pose sequence. Through comprehensive evaluations that include both quantitative and qualitative comparisons, the proposed model demonstrates its competitiveness in prediction accuracy and in-betweening flexibility. Supplementary Material File (cavw_man_document.pdf) Download 12.17 MB Information & Authors Information Version history V1 Version 1 28 April 2025 Peer review timeline Published Computer Animation and Virtual Worlds Version of Record 25 May 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Computer Animation and Virtual Worlds Keywords deep learning keyframe animation motion in-betweening Authors Affiliations Rui Zeng 0000-0001-9688-5875 Beihang University View all articles by this author Ju Dai 0000-0002-9397-8539 [email protected] Peng Cheng Laboratory View all articles by this author Junxuan Bai 0000-0002-7941-0584 Capital University of Physical Education and Sports View all articles by this author Junjun Pan Beihang University View all articles by this author Metrics & Citations Metrics Article Usage 389 views 253 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rui Zeng, Ju Dai, Junxuan Bai, et al. Motion In-betweening via Recursive Keyframe Prediction. Authorea . 28 April 2025. DOI: https://doi.org/10.22541/au.174582061.11145395/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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