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[Human3.6M]Comparisons of different frames between RTN and our method. Both models are trained with 300,000 iterations.

[Human3.6M]Comparisons of different frames between RTN and our method. Both models are trained with 300,000 iterations.

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Fig. 2. When training the CVAE, the encoder takes frames í µí±– and í...
Comparisons of foot skating between position-based representation and...
[Human3.6M]Comparisons of different frames between RTN and our method....
Comparisons between our method and the transformer.
Comparisons of different kinds of actions. The number of frames of the...
Real-time Controllable Motion Transition for Characters
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  • May 2022
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...
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... compare our method with RTN on the Human3.6M dataset, shown in Table 4. While RTN achieves slightly better results in NPSS by as large as 7.6%, our method outperforms RTN in both the í µí°¿ 2 norm and the foot skate by 10.3% and 69.2%, respectively. ...

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