  We present a novel framework of motion tracking from event data using
implicit expression. Our framework use pre-trained event generation MLP named
implicit event generator (IEG) and does motion tracking by updating its state
(position and velocity) based on the difference between the observed event and
generated event from the current state estimate. The difference is computed
implicitly by the IEG. Unlike the conventional explicit approach, which
requires dense computation to evaluate the difference, our implicit approach
realizes efficient state update directly from sparse event data. Our sparse
algorithm is especially suitable for mobile robotics applications where
computational resources and battery life are limited. To verify the
effectiveness of our method on real-world data, we applied it to the AR marker
tracking application. We have confirmed that our framework works well in
real-world environments in the presence of noise and background clutter.
