  Synthesizing natural human motions that enable a 3D human avatar to walk and
reach for arbitrary goals in 3D space remains an unsolved problem with many
applications. Existing methods (data-driven or using reinforcement learning)
are limited in terms of generalization and motion naturalness. A primary
obstacle is the scarcity of training data that combines locomotion with goal
reaching. To address this, we introduce WANDR, a data-driven model that takes
an avatar's initial pose and a goal's 3D position and generates natural human
motions that place the end effector (wrist) on the goal location. To solve
this, we introduce novel intention features that drive rich goal-oriented
movement. Intention guides the agent to the goal, and interactively adapts the
generation to novel situations without needing to define sub-goals or the
entire motion path. Crucially, intention allows training on datasets that have
goal-oriented motions as well as those that do not. WANDR is a conditional
Variational Auto-Encoder (c-VAE), which we train using the AMASS and CIRCLE
datasets. We evaluate our method extensively and demonstrate its ability to
generate natural and long-term motions that reach 3D goals and generalize to
unseen goal locations. Our models and code are available for research purposes
at wandr.is.tue.mpg.de.
