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SIGGRAPH 2018 Mode-Adaptive Neural Networks for Quadruped Motion Control - Sebastian Starke
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#characteranimation#neuralnetworks#unity
[SIGGRAPH 2018] Mode-Adaptive Neural Networks for Quadruped Motion Control
Sebastian Starke
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- Recommended by 1 person: 1 in engineering communities.
- Presented by Sebastian Starke, researcher at University of Edinburgh
- Linked in the descriptionsebastianstarke/AI4Animation
- May be out of date. Uploaded 8 years ago, in an area where tools change fast.
Advanced. Deep dive for people who know the field.
156K views8 years ago1 recommendation
Graphics and audioAI and machine learningGame programmingNeural networks and deep learning
Sebastian Starke demonstrates a deep learning system trained on motion capture data to synthesize realistic quadruped animations in real time.
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 from 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. GitHub: github.com/sebastianstarke/AI4Animation Paper: github.com/sebastianstarke/AI4Animation/blob/master/Media/SI… Windows Demo: starke-consult.de/UoE/GitHub/SIGGRAPH_2018/Demo_Windows.zip Linux Demo: starke-consult.de/UoE/GitHub/SIGGRAPH_2018/Demo_Linux.zip Mac Demo: starke-consult.de/UoE/GitHub/SIGGRAPH_2018/Demo_Mac.zip ReadMe: github.com/sebastianstarke/AI4Animation/blob/master/AI4Anima…Why it's here
1 person recommends thisWhat people say. Recommended as a breakthrough paper in using neural networks for real-time character animation.