  We introduce Temporal Residual Jacobians as a novel representation to enable
data-driven motion transfer. Our approach does not assume access to any rigging
or intermediate shape keyframes, produces geometrically and temporally
consistent motions, and can be used to transfer long motion sequences. Central
to our approach are two coupled neural networks that individually predict local
geometric and temporal changes that are subsequently integrated, spatially and
temporally, to produce the final animated meshes. The two networks are jointly
trained, complement each other in producing spatial and temporal signals, and
are supervised directly with 3D positional information. During inference, in
the absence of keyframes, our method essentially solves a motion extrapolation
problem. We test our setup on diverse meshes (synthetic and scanned shapes) to
demonstrate its superiority in generating realistic and natural-looking
animations on unseen body shapes against SoTA alternatives. Supplemental video
and code are available at https://temporaljacobians.github.io/ .
