  We propose united implicit functions (UNIF), a part-based method for clothed
human reconstruction and animation with raw scans and skeletons as the input.
Previous part-based methods for human reconstruction rely on ground-truth part
labels from SMPL and thus are limited to minimal-clothed humans. In contrast,
our method learns to separate parts from body motions instead of part
supervision, thus can be extended to clothed humans and other articulated
objects. Our Partition-from-Motion is achieved by a bone-centered
initialization, a bone limit loss, and a section normal loss that ensure stable
part division even when the training poses are limited. We also present a
minimal perimeter loss for SDF to suppress extra surfaces and part overlapping.
Another core of our method is an adjacent part seaming algorithm that produces
non-rigid deformations to maintain the connection between parts which
significantly relieves the part-based artifacts. Under this algorithm, we
further propose "Competing Parts", a method that defines blending weights by
the relative position of a point to bones instead of the absolute position,
avoiding the generalization problem of neural implicit functions with inverse
LBS (linear blend skinning). We demonstrate the effectiveness of our method by
clothed human body reconstruction and animation on the CAPE and the ClothSeq
datasets.
