REACTO: Reconstructing Articulated Objects from a Single Video
Chaoyue Song Jiacheng Wei Chuan-Sheng Foo Guosheng Lin Fayao Liu
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:
https://chaoyuesong.github.io/REACTO.