We present a learning method for predicting animation skeletons for input 3D
models of articulated characters. In contrast to previous approaches that fit
pre-defined skeleton templates or predict fixed sets of joints, our method
produces an animation skeleton tailored for the structure and geometry of the
input 3D model. Our architecture is based on a stack of hourglass modules
trained on a large dataset of 3D rigged characters mined from the web. It
operates on the volumetric representation of the input 3D shapes augmented with
geometric shape features that provide additional cues for joint and bone
locations. Our method also enables intuitive user control of the
level-of-detail for the output skeleton. Our evaluation demonstrates that our
approach predicts animation skeletons that are much more similar to the ones
created by humans compared to several alternatives and baselines.

本文提出了一种基于堆叠沙漏模型的学习方法，用于预测具有关节的 3D 角色模型的动画骨骼系统，通过对从网络上挖掘的大型 3D 角色数据集进行训练，得到的方法非常类似于人类创作，同时保证骨骼系统与输入的 3D 模型具有结构和几何的匹配性，具有较强的可控性。

通过容积网络预测 3D 关节动画骨骼

Predicting Animation Skeletons for 3D Articulated Models via Volumetric  Nets

Deep learning based single image super-resolution methods use a large number
of training datasets and have recently achieved great quality progress both
quantitatively and qualitatively. Most deep networks focus on nonlinear mapping
from low-resolution inputs to high-resolution outputs via residual learning
without exploring the feature abstraction and analysis. We propose a
Hierarchical Back Projection Network (HBPN), that cascades multiple HourGlass
(HG) modules to bottom-up and top-down process features across all scales to
capture various spatial correlations and then consolidates the best
representation for reconstruction. We adopt the back projection blocks in our
proposed network to provide the error correlated up and down-sampling process
to replace simple deconvolution and pooling process for better estimation. A
new Softmax based Weighted Reconstruction (WR) process is used to combine the
outputs of HG modules to further improve super-resolution. Experimental results
on various datasets (including the validation dataset, NTIRE2019, of the Real
Image Super-resolution Challenge) show that our proposed approach can achieve
and improve the performance of the state-of-the-art methods for different
scaling factors.

本文提出了一种基于深度学习和特征分析的超分辨率技术 Hierarchical Back Projection Network (HBPN)，采用多个 HourGlass 模块进行自底向上和自顶向下的跨尺度特征处理，并使用新的基于 Softmax 的加权重建方法进一步提高超分辨率性能。实验结果表明，该方法可以实现超分辨率问题的优异表现，并在不同尺度因子下具有更好的性能。