From Ground to Sky: Flying-motion Generation via Motion Dataset Adaptation

We conducted a study utilizing a lightweight generative network to create flying motions. The existing datasets used for training did not include any data on flying motions. Therefore, we selected certain classes from the existing motion datasets and transformed these motions to resemble flying actions. By training the existing generative network with the modified dataset, we were able to generate motions that closely resemble flying. The results of this study demonstrate the potential for generating flying motions. The generation of flying motions for human avatars is expected to be a critical technology not only in 3D animation or game industry but also in virtual environments, enabling users to experience various activities through their avatars.