  Synthesizing interaction-involved human motions has been challenging due to
the high complexity of 3D environments and the diversity of possible human
behaviors within. We present LAMA, Locomotion-Action-MAnipulation, to
synthesize natural and plausible long-term human movements in complex indoor
environments. The key motivation of LAMA is to build a unified framework to
encompass a series of everyday motions including locomotion, scene interaction,
and object manipulation. Unlike existing methods that require motion data
"paired" with scanned 3D scenes for supervision, we formulate the problem as a
test-time optimization by using human motion capture data only for synthesis.
LAMA leverages a reinforcement learning framework coupled with a motion
matching algorithm for optimization, and further exploits a motion editing
framework via manifold learning to cover possible variations in interaction and
manipulation. Throughout extensive experiments, we demonstrate that LAMA
outperforms previous approaches in synthesizing realistic motions in various
challenging scenarios. Project page: https://jiyewise.github.io/projects/LAMA/ .
