Real-time Controllable Motion Transition for Characters

• URL: http://arxiv.org/abs/2205.02540v1
• Date: Thu, 5 May 2022 10:02:54 GMT
• Title: Real-time Controllable Motion Transition for Characters
• Authors: Xiangjun Tang, He Wang, Bo Hu, Xu Gong, Ruifan Yi, Qilong Kou, Xiaogang Jin
• Abstract summary: Real-time in-between motion generation is universally required in games and highly desirable in existing animation pipelines. Our approach consists of two key components: motion manifold and conditional transitioning. We show that our method is able to generate high-quality motions measured under multiple metrics.
• Score: 14.88407656218885
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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