  We present HIPNet, a neural implicit pose network trained on multiple
subjects across many poses. HIPNet can disentangle subject-specific details
from pose-specific details, effectively enabling us to retarget motion from one
subject to another or to animate between keyframes through latent space
interpolation. To this end, we employ a hierarchical skeleton-based
representation to learn a signed distance function on a canonical unposed
space. This joint-based decomposition enables us to represent subtle details
that are local to the space around the body joint. Unlike previous neural
implicit method that requires ground-truth SDF for training, our model we only
need a posed skeleton and the point cloud for training, and we have no
dependency on a traditional parametric model or traditional skinning
approaches. We achieve state-of-the-art results on various single-subject and
multi-subject benchmarks.
