  Aligning a template to 3D human point clouds is a long-standing problem
crucial for tasks like animation, reconstruction, and enabling supervised
learning pipelines. Recent data-driven methods leverage predicted surface
correspondences. However, they are not robust to varied poses, identities, or
noise. In contrast, industrial solutions often rely on expensive manual
annotations or multi-view capturing systems. Recently, neural fields have shown
promising results. Still, their purely data-driven and extrinsic nature does
not incorporate any guidance toward the target surface, often resulting in a
trivial misalignment of the template registration. Currently, no method can be
considered the standard for 3D Human registration, limiting the scalability of
downstream applications. In this work, we propose a neural scalable
registration method, NSR, a pipeline that, for the first time, generalizes and
scales across thousands of shapes and more than ten different data sources. Our
essential contribution is NICP, an ICP-style self-supervised task tailored to
neural fields. NSR takes a few seconds, is self-supervised, and works out of
the box on pre-trained neural fields. NSR combines NICP with a localized neural
field trained on a large MoCap dataset, achieving the state of the art over
public benchmarks. The release of our code and checkpoints provides a powerful
tool useful for many downstream tasks like dataset alignments, cleaning, or
asset animation.
