Visual fidelity in games has improved massively in the last few years, to the point where ray-tracing is now being used to complement rasterisation in real time. Animation, however, has lagged behind, but a combo of motion capture and neural networks could change that.
If you ever want a sneak peek at the technology games will be using over the next decade, just check out SIGGRAPH. For instance, at this year’s conference, University of Edinburgh animation and AI student Sebastian Starke will be showing off this neat bit of work.
Called “AI4Animation”, the system “can produce natural animations from real motion data” via “a novel neural network architecture, called Mode-Adaptive Neural Networks”:
Instead of optimising a fixed group of weights, the system learns to dynamically blend a group of weights into a further neural network, based on the current state of the character.
That said, the system does not require labels for the phase or locomotion gaits, but can learn from unstructured motion capture data in an end-to-end fashion.
The final product is smooth, realistic animations, minus the painstaking work of doing everything by hand. Interactive demos are available for Windows, macOS and Linux, so you can play around with it yourself.
In other news, I think we’re about ready for a new Okami, Capcom.
AI4Animation [GitHub]
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