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• W2963310566 abstract "We present a novel approach, based on deep recurrent neural networks, to automatically generate transition animations given a past context of a few frames, a target character state and optionally local terrain information. The proposed Recurrent Transition Network (RTN) is trained without any gait, phase, contact or action labels. Our system produces realistic and fluid transitions that rival the quality of Motion Capture-based animations, even without any inverse-kinematics post-process. Our system could accelerate the creation of transition variations for large coverage or even replace transition nodes in a game's animation graph. The RTN also shows impressive results on a temporal super-resolution task." @default.
• W2963310566 created "2019-07-30" @default.
• W2963310566 creator A5015264496 @default.
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• W2963310566 date "2018-12-04" @default.
• W2963310566 modified "2023-09-29" @default.
• W2963310566 title "Recurrent transition networks for character locomotion" @default.
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• W2963310566 doi "https://doi.org/10.1145/3283254.3283277" @default.
• W2963310566 hasPublicationYear "2018" @default.
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