We present a system for learning full-body neural avatars, i.e. deep networks
that produce full-body renderings of a person for varying body pose and camera
position. Our system takes the middle path between the classical graphics
pipeline and the recent deep learning approaches that generate images of humans
using image-to-image translation. In particular, our system estimates an
explicit two-dimensional texture map of the model surface. At the same time, it
abstains from explicit shape modeling in 3D. Instead, at test time, the system
uses a fully-convolutional network to directly map the configuration of body
feature points w.r.t. the camera to the 2D texture coordinates of individual
pixels in the image frame. We show that such a system is capable of learning to
generate realistic renderings while being trained on videos annotated with 3D
poses and foreground masks. We also demonstrate that maintaining an explicit
texture representation helps our system to achieve better generalization
compared to systems that use direct image-to-image translation.

我们提出了一个学习全身神经头像的系统，该系统采用中间路径，同时估计模型表面的显式二维纹理映射，并通过卷积网络将身体特征点的配置与相机相对应，直接映射为图像中单个像素的 2D 纹理坐标。我们展示了这样的系统能够学习生成逼真的渲染，并通过与使用直接图像到图像翻译的系统相比较的值得推崇的泛化性能来展示，保持显式纹理表示对于系统的改善有所帮助。