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

We present a method and application for animating a human subject from a
single photo. E.g., the character can walk out, run, sit, or jump in 3D. The
key contributions of this paper are: 1) an application of viewing and animating
humans in single photos in 3D, 2) a novel 2D warping method to deform a posable
template body model to fit the person's complex silhouette to create an
animatable mesh, and 3) a method for handling partial self occlusions. We
compare to state-of-the-art related methods and evaluate results with human
studies. Further, we present an interactive interface that allows re-posing the
person in 3D, and an augmented reality setup where the animated 3D person can
emerge from the photo into the real world. We demonstrate the method on photos,
posters, and art.

本文提出单张照片中制作一个可以在三维中行走、奔跑、坐下或者跳跃的角色的方法和应用程序。主要贡献包括三个方面：1）在单张照片中浏览和动画化人类角色的应用程序，2）一种新的二维变形方法，将可摆放的模板人体模型变形以适应人的复杂轮廓，创造可动画的网格，3）处理部分自遮挡的方法。此外，我们还提供了一种交互式界面，允许在 3D 中重新调整人物的姿态。我们在人物研究中进行了结果评估。最后，我们还展示了一个增强现实设置，其中动画化的 3D 角色可以从照片中出现到真实世界中。我们将此方法应用于照片、海报和艺术品上。

单张照片生成 3D 角色动画

Photo Wake-Up: 3D Character Animation from a Single Photo

Despite remarkable advances in image synthesis research, existing works often
fail in manipulating images under the context of large geometric
transformations. Synthesizing person images conditioned on arbitrary poses is
one of the most representative examples where the generation quality largely
relies on the capability of identifying and modeling arbitrary transformations
on different body parts. Current generative models are often built on local
convolutions and overlook the key challenges (e.g. heavy occlusions, different
views or dramatic appearance changes) when distinct geometric changes happen
for each part, caused by arbitrary pose manipulations. This paper aims to
resolve these challenges induced by geometric variability and spatial
displacements via a new Soft-Gated Warping Generative Adversarial Network
(Warping-GAN), which is composed of two stages: 1) it first synthesizes a
target part segmentation map given a target pose, which depicts the
region-level spatial layouts for guiding image synthesis with higher-level
structure constraints; 2) the Warping-GAN equipped with a soft-gated
warping-block learns feature-level mapping to render textures from the original
image into the generated segmentation map. Warping-GAN is capable of
controlling different transformation degrees given distinct target poses.
Moreover, the proposed warping-block is light-weight and flexible enough to be
injected into any networks. Human perceptual studies and quantitative
evaluations demonstrate the superiority of our Warping-GAN that significantly
outperforms all existing methods on two large datasets.

本文介绍了 Soft-Gated Warping Generative Adversarial Network（Warping-GAN）的开发，该网络利用了灵活轻便的规避块，以在生产人像时处理任意姿势等大尺度几何变换，并在两个大型数据集上得到了显着优越的表现。