  Our goal is to populate digital environments, in which digital humans have
diverse body shapes, move perpetually, and have plausible body-scene contact.
The core challenge is to generate realistic, controllable, and infinitely long
motions for diverse 3D bodies. To this end, we propose generative motion
primitives via body surface markers, or GAMMA in short. In our solution, we
decompose the long-term motion into a time sequence of motion primitives. We
exploit body surface markers and conditional variational autoencoder to model
each motion primitive, and generate long-term motion by implementing the
generative model recursively. To control the motion to reach a goal, we apply a
policy network to explore the generative model's latent space and use a
tree-based search to preserve the motion quality during testing. Experiments
show that our method can produce more realistic and controllable motion than
state-of-the-art data-driven methods. With conventional path-finding
algorithms, the generated human bodies can realistically move long distances
for a long period of time in the scene. Code is released for research purposes
at: \url{https://yz-cnsdqz.github.io/eigenmotion/GAMMA/}
