Abstract
We propose a new framework for creating and easily manipulating 3D models of arbitrary objects using casually captured videos. Our core ingredient is a novel hierarchy deformation model, which captures motions of objects with a tree-structured bones. Our hierarchy system decomposes motions based on the granularity and reveals the correlations between parts without exploiting any prior structural knowledge. We further propose to regularize the bones to be positioned at the basis of motions, centers of parts, sufficiently covering related surfaces of the part. This is achieved by our bone occupancy function, which identifies whether a given 3D point is placed within the bone. Coupling the proposed components, our framework offers several clear advantages: (1) users can obtain animatable 3D models of the arbitrary objects in improved quality from their casual videos, (2) users can manipulate 3D models in an intuitive manner with minimal costs, and (3) users can interactively add or delete control points as necessary. The experimental results demonstrate the efficacy of our framework on diverse instances, in reconstruction quality, interpretability and easier manipulation.
Overview
We aim to reconstruct animatable models that can be manipulated in a coarse-to-fine manner, using multiple videos capturing a deformable object.
The resulting 3D model can be manipulated using a hierarchical deformation model, where coarse motions are manipulated using the parent bones, and fine motions are subdivided by the child bones.
We present manipulation results in novel poses.
Method
(a) The overview of the proposed framework for creating 3D animatble models from videos. Each ray from the image pixel is deformed to the canonical space. Rays are deformed in a coarse-to-fine manner, using the hierarchical neural deformation model
(b) The process of hierarchical neural deformation model. Coarse motions and fine motions are composited through the bone hierarchy formulation.
Results
3D Reconstruction and Bones
Manipulation Comparison
Motion at each depth
BibTeX
@article{jeon2024hsnb,
author = {Jeon, Subin and Cho, In and Kim, Minsu and Cho, Woong Oh and Kim, Seon Joo},
title = {Hierarchically Structured Neural Bones for Reconstructing Animatable Objects from Casual Videos},
journal = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
}