  Synthesizing 3D human avatars interacting realistically with a scene is an
important problem with applications in AR/VR, video games and robotics. Towards
this goal, we address the task of generating a virtual human -- hands and full
body -- grasping everyday objects. Existing methods approach this problem by
collecting a 3D dataset of humans interacting with objects and training on this
data. However, 1) these methods do not generalize to different object positions
and orientations, or to the presence of furniture in the scene, and 2) the
diversity of their generated full-body poses is very limited. In this work, we
address all the above challenges to generate realistic, diverse full-body
grasps in everyday scenes without requiring any 3D full-body grasping data. Our
key insight is to leverage the existence of both full-body pose and hand
grasping priors, composing them using 3D geometrical constraints to obtain
full-body grasps. We empirically validate that these constraints can generate a
variety of feasible human grasps that are superior to baselines both
quantitatively and qualitatively. See our webpage for more details:
https://flex.cs.columbia.edu/.
