In this paper, we address the challenge of reconstructing general articulated
3D objects from a single video. Existing works employing dynamic neural
radiance fields have advanced the modeling of articulated objects like humans
and animals from videos, but face challenges with piece-wise rigid general
articulated objects due to limitations in their deformation models. To tackle
this, we propose Quasi-Rigid Blend Skinning, a novel deformation model that
enhances the rigidity of each part while maintaining flexible deformation of
the joints. Our primary insight combines three distinct approaches: 1) an
enhanced bone rigging system for improved component modeling, 2) the use of
quasi-sparse skinning weights to boost part rigidity and reconstruction
fidelity, and 3) the application of geodesic point assignment for precise
motion and seamless deformation. Our method outperforms previous works in
producing higher-fidelity 3D reconstructions of general articulated objects, as
demonstrated on both real and synthetic datasets. Project page:
this https URL

从单一视频中重构一般关节式 3D 物体的挑战已被克服，引入了新的变形模型，通过优化部件建模、加强刚性以及精确运动和无缝变形，提高了一般关节式物体的 3D 重建质量。