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Video: Video: [SIGGRAPH 2018] Mode-Adaptive Neural Networks for Quadruped Motion Control

We present a novel method for real-time quadruped motion synthesis called Mode-Adaptive Neural Networks. Our system is trained in an end-to-end fashion on unstructured motion capture data, without requiring labels for the phase or locomotion gaits. The system can be used for creating natural animations in games and films, and is the first of such systematic approaches whose quality could be of practical use. It is implemented in the Unity 3D engine and TensorFlow, and published under the ACM Transactions on Graphics / SIGGRAPH 2018. Interactive Demo: ...will be available very soon... GitHub: https://github.com/sebastianstarke/AI4Animation Paper: https://github.com/sebastianstarke/AI4Animation/blob/master/Media/SIGGRAPH_2018/Paper.pdf
Sebastian StarkeartificialScience & TechnologyCharacter AnimationQuadrupedsNeural NetworksArtificial IntelligenceDeep LearningSIGGRAPHCharacter ControlUnity3DTensorFlowUniversity of EdinburghMotion CaptureMotion

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[SIGGRAPH 2018] Mode-Adaptive Neural Networks for Quadruped Motion Control