  The motion of picking up and placing an object in 3D space is full of subtle
detail. Typically these motions are formed from the same constraints,
optimizing for swiftness, energy efficiency, as well as physiological limits.
Yet, even for identical goals, the motion realized is always subject to natural
variation. To capture these aspects computationally, we suggest a deep
generative model for human reach-and-place action, conditioned on a start and
end position.We have captured a dataset of 600 such human 3D actions, to sample
the 2x3-D space of 3D source and targets. While temporal variation is often
modeled with complex learning machinery like recurrent neural networks or
networks with memory or attention, we here demonstrate a much simpler approach
that is convolutional in time and makes use of(periodic) temporal encoding.
Provided a latent code and conditioned on start and end position, the model
generates a complete 3D character motion in linear time as a sequence of
convolutions. Our evaluation includes several ablations, analysis of generative
diversity and applications.
