UNIF: United Neural Implicit Functions for Clothed Human Reconstruction and Animation

TL;DR

UNIF introduces a part-based neural implicit function approach for clothed human reconstruction and animation, overcoming limitations of prior methods by learning part separation from motion and ensuring smooth part connections.

Contribution

The paper presents a novel part-based method that learns to separate body parts from motion without part labels, enabling clothed human reconstruction and animation.

Findings

Effective reconstruction and animation on CAPE and ClothSeq datasets.

Outperforms previous part-based methods in clothed human scenarios.

Reduces artifacts and improves part connection stability.

Abstract

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.