Given a single image of a general object such as a chair, could we also
restore its articulated 3D shape similar to human modeling, so as to animate
its plausible articulations and diverse motions? This is an interesting new
question that may have numerous downstream augmented reality and virtual
reality applications. Comparing with previous efforts on object manipulation,
our work goes beyond 2D manipulation and rigid deformation, and involves
articulated manipulation. To achieve this goal, we propose an automated
approach to build such 3D generic objects from single images and embed
articulated skeletons in them. Specifically, our framework starts by
reconstructing the 3D object from an input image. Afterwards, to extract
skeletons for generic 3D objects, we develop a novel skeleton prediction method
with a multi-head structure for skeleton probability field estimation by
utilizing the deep implicit functions. A dataset of generic 3D objects with
ground-truth annotated skeletons is collected. Empirically our approach is
demonstrated with satisfactory performance on public datasets as well as our
in-house dataset; our results surpass those of the state-of-the-arts by a
noticeable margin on both 3D reconstruction and skeleton prediction.

通过单个图像的三维重建，提出自动化构建三维对象并嵌入关节骨架的框架，结合深度隐式函数的多头结构骨架概率场估计方法进行骨架提取预测，并在公共数据集和专有数据集上表现出良好性能。

从单张图像实现的 3D 物体绑定

Object Wake-up: 3D Object Rigging from a Single Image

We present RigNet, an end-to-end automated method for producing animation
rigs from input character models. Given an input 3D model representing an
articulated character, RigNet predicts a skeleton that matches the animator
expectations in joint placement and topology. It also estimates surface skin
weights based on the predicted skeleton. Our method is based on a deep
architecture that directly operates on the mesh representation without making
assumptions on shape class and structure. The architecture is trained on a
large and diverse collection of rigged models, including their mesh, skeletons
and corresponding skin weights. Our evaluation is three-fold: we show better
results than prior art when quantitatively compared to animator rigs;
qualitatively we show that our rigs can be expressively posed and animated at
multiple levels of detail; and finally, we evaluate the impact of various
algorithm choices on our output rigs.

该研究提出 RigNet：一种基于深度学习的自动化动画角色建模软件，通过输入 3D 模型可以精确预测骨骼结构和表面权重，并且可以比其他算法更好地模拟动态效果。

RigNet: 基于神经网络的关节角色绑定

RigNet: Neural Rigging for Articulated Characters

We present a method that gets as input an audio of violin or piano playing,
and outputs a video of skeleton predictions which are further used to animate
an avatar. The key idea is to create an animation of an avatar that moves their
hands similarly to how a pianist or violinist would do, just from audio. Aiming
for a fully detailed correct arms and fingers motion is a goal, however, it's
not clear if body movement can be predicted from music at all. In this paper,
we present the first result that shows that natural body dynamics can be
predicted at all. We built an LSTM network that is trained on violin and piano
recital videos uploaded to the Internet. The predicted points are applied onto
a rigged avatar to create the animation.

本研究提出了一种方法，可以将小提琴或钢琴的演奏声音转化为骨架预测视频，进而用于动画化头像。研究表明，基于从互联网上传的小提琴和钢琴演奏视频训练的 LSTM 网络可以预测自然的身体动态，并将预测出的点应用于装配的角色机器人，以创建动画。