NICP: Neural ICP for 3D Human Registration at Scale
arxiv(2023)
Abstract
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 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. NICP takes a few seconds, is
self-supervised, and works out of the box on pre-trained neural fields. We
combine it with a localized Neural Field trained on a large MoCap dataset. NSR
achieves the state of the art over public benchmarks, and the release of its
code and checkpoints will provide the community with a powerful tool useful for
many downstream tasks like dataset alignments, cleaning, or asset animation.
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