We address the problem of building digital twins of unknown articulated
objects from two RGBD scans of the object at different articulation states. We
decompose the problem into two stages, each addressing distinct aspects. Our
method first reconstructs object-level shape at each state, then recovers the
underlying articulation model including part segmentation and joint
articulations that associate the two states. By explicitly modeling point-level
correspondences and exploiting cues from images, 3D reconstructions, and
kinematics, our method yields more accurate and stable results compared to
prior work. It also handles more than one movable part and does not rely on any
object shape or structure priors. Project page:
this https URL

我们通过两个不同关节状态的物体的两个 RGBD 扫描构建未知关节物体的数字孪生问题，通过分解成两个阶段并采用显式建模，我们的方法能够重建物体形状并恢复关节模型，通过利用图像、3D 重建和运动学信息，我们的方法相比之前的工作具有更高的准确性和稳定性，并且能够处理多个活动部件并不依赖于任何物体形状或结构的先验知识。

未知关节物体的数字双胞胎建模的神经隐式表示

Neural Implicit Representation for Building Digital Twins of Unknown  Articulated Objects

We present the first method that automatically transfers poses between
stylized 3D characters without skeletal rigging. In contrast to previous
attempts to learn pose transformations on fixed or topology-equivalent skeleton
templates, our method focuses on a novel scenario to handle skeleton-free
characters with diverse shapes, topologies, and mesh connectivities. The key
idea of our method is to represent the characters in a unified articulation
model so that the pose can be transferred through the correspondent parts. To
achieve this, we propose a novel pose transfer network that predicts the
character skinning weights and deformation transformations jointly to
articulate the target character to match the desired pose. Our method is
trained in a semi-supervised manner absorbing all existing character data with
paired/unpaired poses and stylized shapes. It generalizes well to unseen
stylized characters and inanimate objects. We conduct extensive experiments and
demonstrate the effectiveness of our method on this novel task.

本文提出了一种自动将姿势转移至无骨骼的、具有多样形状、拓扑和网格连接性的风格化 3D 角色的方法，并通过新颖的统一关节模型，使用一个姿势转移网络来预测角色的皮肤绑定权重和变形变换，以匹配所需的姿势。该方法采用半监督训练方式，且具有良好的泛化能力，可广泛应用于风格化角色和非生动物体的动画制作。

无需骨架转移姿势到风格化 3D 角色

Skeleton-free Pose Transfer for Stylized 3D Characters

Robots in human environments will need to interact with a wide variety of
articulated objects such as cabinets, drawers, and dishwashers while assisting
humans in performing day-to-day tasks. Existing methods either require objects
to be textured or need to know the articulation model category a priori for
estimating the model parameters for an articulated object. We propose ScrewNet,
a novel approach that estimates an object's articulation model directly from
depth images without requiring a priori knowledge of the articulation model
category. ScrewNet uses screw theory to unify the representation of different
articulation types and perform category-independent articulation model
estimation. We evaluate our approach on two benchmarking datasets and compare
its performance with a current state-of-the-art method. Results demonstrate
that ScrewNet can successfully estimate the articulation models and their
parameters for novel objects across articulation model categories with better
on average accuracy than the prior state-of-the-art method. Project webpage:
this https URL

本文提出了一种名为 ScrewNet 的方法，可以直接估计物体的运动模型，从而帮助机器人在人类环境中与各种关节物体如橱柜、抽屉和洗碗机进行交互。该方法利用螺旋理论统一不同类型的运动，通过无需先验知识的深度图像来进行模型估计。在两个基准数据集上的评估结果表明，ScrewNet 能够成功地估计不同运动模型下的新物体，且平均精度较之前最先进的方法更好。