Researchers from University of Edinburgh and Method Studios developed a real-time character control mechanism using deep learning that can help virtual characters walk, run and jump a little more naturally.
“Data-driven motion synthesis using neural networks is attracting researchers in both the computer animation and machine learning communities thanks to its high scalability and runtime efficiency,” mentioned the researchers in their paper.
Using CUDA, NVIDIA GeForce GPUs and cuDNN with the Theano deep learning framework, their “Phase-Functioned Neural Network” is a time-series approach that can predict the pose of the character given the user inputs and the previous state of the character. The system is trained on data that includes the character moving over different terrains which then helps the character automatically adapt to the geometry of the environment during runtime.
The innovative work by Daniel Holden (now a researcher at Ubisoft Montreal), Taku Komura (University of Edinburgh) and Jun Saito (Method Studios) can possibly change the future of video game development.
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Human-like Character Animation System Uses AI to Navigate Terrains
May 02, 2017
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AI-Generated Summary
- Researchers from the University of Edinburgh and Method Studios created a real-time character control system using a Phase-Functioned Neural Network that predicts character poses from user input and previous state.
- The network was trained on motion data across varied terrains so characters automatically adapt to environmental geometry during runtime.
- Training and inference used CUDA, NVIDIA GeForce GPUs, and cuDNN with the Theano deep learning framework.
- Daniel Holden, Taku Komura, and Jun Saito authored the work, which demonstrates a scalable, data-driven approach to motion synthesis for games and animation.
Next Steps
- Read the Phase-Functioned Neural Network paper for the full technical details.
- Read the TechCrunch coverage for an overview of the research and its potential impact.
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