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.
@article{arxiv.2112.00958,
title = {Hierarchical Neural Implicit Pose Network for Animation and Motion Retargeting},
author = {Sourav Biswas and Kangxue Yin and Maria Shugrina and Sanja Fidler and Sameh Khamis},
journal= {arXiv preprint arXiv:2112.00958},
year = {2021}
}