Tang, X, Wang, H orcid.org/0000-0002-2281-5679, Hu, B et al. (4 more authors) (2022) Real-time controllable motion transition for characters. ACM Transactions on Graphics, 41 (4). 137. ISSN 0730-0301
Abstract
Real-time in-between motion generation is universally required in games and highly desirable in existing animation pipelines. Its core challenge lies in the need to satisfy three critical conditions simultaneously: quality, controllability and speed, which renders any methods that need offline computation (or post-processing) or cannot incorporate (often unpredictable) user control undesirable. To this end, we propose a new real-time transition method to address the aforementioned challenges. Our approach consists of two key components: motion manifold and conditional transitioning. The former learns the important low-level motion features and their dynamics; while the latter synthesizes transitions conditioned on a target frame and the desired transition duration. We first learn a motion manifold that explicitly models the intrinsic transition stochasticity in human motions via a multi-modal mapping mechanism. Then, during generation, we design a transition model which is essentially a sampling strategy to sample from the learned manifold, based on the target frame and the aimed transition duration. We validate our method on different datasets in tasks where no post-processing or offline computation is allowed. Through exhaustive evaluation and comparison, we show that our method is able to generate high-quality motions measured under multiple metrics. Our method is also robust under various target frames (with extreme cases).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | in-betweening, real-time, animation, motion manifold, deep learning, conditional transitioning, locomotion |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 May 2022 12:02 |
Last Modified: | 28 Mar 2023 15:48 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3528223.3530090 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186288 |