In this paper, we propose a deep learning-based method to segment the
skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which
can assist in the assessment of drug-induced skeletal abnormalities as a
required study in developmental and reproductive toxicology (DART). Our
strategy leverages sub-optimal segmentation labels of 22 skull bones from 26
micro-CT volumes and maps them to 250 unlabeled volumes on which a deep
CNN-based segmentation model is trained. In the experiments, our model was able
to achieve an average Dice Similarity Coefficient (DSC) of 0.89 across all
bones on the testing set, and 14 out of the 26 skull bones reached average DSC
>0.93. Our next steps are segmenting the whole body followed by developing a
model to classify abnormalities.

本研究提出一种基于深度学习的方法，用于分割荷兰斑带兔胎儿的微 CT 图像中的骨骼结构，以协助评估药物诱导的骨骼异常，实现毒理学研究的必备环节。

基于深度学习的兔胎儿颅骨分割，使用有限和次优标注

Deep learning-based Segmentation of Rabbit fetal skull with limited and  sub-optimal annotations

We present Neural Marionette, an unsupervised approach that discovers the
skeletal structure from a dynamic sequence and learns to generate diverse
motions that are consistent with the observed motion dynamics. Given a video
stream of point cloud observation of an articulated body under arbitrary
motion, our approach discovers the unknown low-dimensional skeletal
relationship that can effectively represent the movement. Then the discovered
structure is utilized to encode the motion priors of dynamic sequences in a
latent structure, which can be decoded to the relative joint rotations to
represent the full skeletal motion. Our approach works without any prior
knowledge of the underlying motion or skeletal structure, and we demonstrate
that the discovered structure is even comparable to the hand-labeled ground
truth skeleton in representing a 4D sequence of motion. The skeletal structure
embeds the general semantics of possible motion space that can generate motions
for diverse scenarios. We verify that the learned motion prior is generalizable
to the multi-modal sequence generation, interpolation of two poses, and motion
retargeting to a different skeletal structure.

Neural Marionette 是一种无监督学习方法，可以从动态序列中发现骨架结构，学会生成与观察到的运动动态一致的多样化运动，无需先验知识，可泛化到多模态序列生成、两个姿势的插值和运动重新定向。

神经木偶：从体积视频中无监督学习动作骨骼和潜在动力学

Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video

Animating a newly designed character using motion capture (mocap) data is a
long standing problem in computer animation. A key consideration is the
skeletal structure that should correspond to the available mocap data, and the
shape deformation in the joint regions, which often requires a tailored,
pose-specific refinement. In this work, we develop a neural technique for
articulating 3D characters using enveloping with a pre-defined skeletal
structure which produces high quality pose dependent deformations. Our
framework learns to rig and skin characters with the same articulation
structure (e.g., bipeds or quadrupeds), and builds the desired skeleton
hierarchy into the network architecture. Furthermore, we propose neural blend
shapes--a set of corrective pose-dependent shapes which improve the deformation
quality in the joint regions in order to address the notorious artifacts
resulting from standard rigging and skinning. Our system estimates neural blend
shapes for input meshes with arbitrary connectivity, as well as weighting
coefficients which are conditioned on the input joint rotations. Unlike recent
deep learning techniques which supervise the network with ground-truth rigging
and skinning parameters, our approach does not assume that the training data
has a specific underlying deformation model. Instead, during training, the
network observes deformed shapes and learns to infer the corresponding rig,
skin and blend shapes using indirect supervision. During inference, we
demonstrate that our network generalizes to unseen characters with arbitrary
mesh connectivity, including unrigged characters built by 3D artists.
Conforming to standard skeletal animation models enables direct plug-and-play
in standard animation software, as well as game engines.

本文通过开发一种基于神经技术的方法，在不假定训练数据的基础形变模型的情况下，使神经网络学习到骨架、皮肤和混合形状，并构建了一个骨骼层次结构的网络架构，利用预定义的骨骼结构使 3D 角色实现高质量的姿态相关形变，其中提出了用于改进关节区域变形质量的纠正姿势依赖形状的方法。