Xiangjun Tang, He Wang, Bo Hu, Xu Gong, Ruifan Yi, Qilong Kou, Xiaogang Jin
In-between motion sequences (blue) between target frames (orange) generated by our method. Given a target frame and a desired transition duration, the controlled character can dynamically adjust strategies, e.g., different step sizes, velocities, or motion types, to reach the target without visual artifacts.
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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Acknowledgments: The authors would like to thank Sammi (Xia Lin), Eric (Ma Bingbing), Eason (Yang Xiajun), and Rambokou (Kou Qilong) from Tencent Institute of Games for contributing useful data and relevant application demonstration assistance during the paper's preparation, which provided significant support for the paper's completion.