Animating an object in 3D often requires an articulated structure, e.g. a
kinematic chain or skeleton of the manipulated object with proper skinning
weights, to obtain smooth movements and surface deformations. However, existing
models that allow direct pose manipulations are either limited to specific
object categories or built with specialized equipment. To reduce the work
needed for creating animatable 3D models, we propose a novel reconstruction
method that learns an animatable kinematic chain for any articulated object.
Our method operates on monocular videos without prior knowledge of the object's
shape or underlying structure. Our approach is on par with state-of-the-art 3D
surface reconstruction methods on various articulated object categories while
enabling direct pose manipulations by re-posing the learned kinematic chain.

提出一种新的重建方法，可以学习任何物品的动画运动学链，不需要事先了解物品的形状或基础结构，能够实现直接姿势操纵，并在各种复杂的关节对象类型上获得与最先进的三维表面重建方法相当的表现。

从单目视频中构建类别不可知和可动画的三维模型：CAMM

CAMM: Building Category-Agnostic and Animatable 3D Models from Monocular  Videos

Deep learning for predicting or generating 3D human pose sequences is an
active research area. Previous work regresses either joint rotations or joint
positions. The former strategy is prone to error accumulation along the
kinematic chain, as well as discontinuities when using Euler angle or
exponential map parameterizations. The latter requires re-projection onto
skeleton constraints to avoid bone stretching and invalid configurations. This
work addresses both limitations. Our recurrent network, QuaterNet, represents
rotations with quaternions and our loss function performs forward kinematics on
a skeleton to penalize absolute position errors instead of angle errors. On
short-term predictions, QuaterNet improves the state-of-the-art quantitatively.
For long-term generation, our approach is qualitatively judged as realistic as
recent neural strategies from the graphics literature.

本研究利用深度學習方法中的循環神經網路，使用四元數表示旋轉矩陣，並在預測人體姿勢時，通過直接避免產生角度誤差而避免骨骼伸展和無效配置。在短期預測上，QuaterNet 方法可以量化地提高所得到的結果，而在長期模擬方面，本研究方法的效果與近期的網路策略相當。

基于四元数的循环人体动作模型

QuaterNet: A Quaternion-based Recurrent Model for Human Motion

This paper deals with motion capture of kinematic chains (e.g. human
skeletons) from monocular image sequences taken by uncalibrated cameras. We
present a method based on projecting an observation into a kinematic chain
space (KCS). An optimization of the nuclear norm is proposed that implicitly
enforces structural properties of the kinematic chain. Unlike other approaches
our method does not require specific camera or object motion and is not relying
on training data or previously determined constraints such as particular body
lengths. The proposed algorithm is able to reconstruct scenes with limited
camera motion and previously unseen motions. It is not only applicable to human
skeletons but also to other kinematic chains for instance animals or industrial
robots. We achieve state-of-the-art results on different benchmark data bases
and real world scenes.

本文提出了一种基于核范数优化的运动捕捉方法，该方法不需要特定的相机或物体运动，并且不依赖于训练数据或先前确定的约束条件，能够重建具有有限相机运动和先前未见过的运动的情景，适用于人体骨架、动物或工业机器人等其他运动链