While progress in 2D generative models of human appearance has been rapid,
many applications require 3D avatars that can be animated and rendered.
Unfortunately, most existing methods for learning generative models of 3D
humans with diverse shape and appearance require 3D training data, which is
limited and expensive to acquire. The key to progress is hence to learn
generative models of 3D avatars from abundant unstructured 2D image
collections. However, learning realistic and complete 3D appearance and
geometry in this under-constrained setting remains challenging, especially in
the presence of loose clothing such as dresses. In this paper, we propose a new
adversarial generative model of realistic 3D people from 2D images. Our method
captures shape and deformation of the body and loose clothing by adopting a
holistic 3D generator and integrating an efficient and flexible articulation
module. To improve realism, we train our model using multiple discriminators
while also integrating geometric cues in the form of predicted 2D normal maps.
We experimentally find that our method outperforms previous 3D- and
articulation-aware methods in terms of geometry and appearance. We validate the
effectiveness of our model and the importance of each component via systematic
ablation studies.

本文提出了一种从 2D 图像生成逼真的 3D 人物的新方法，并使用多个辨别器和 2D 法线图形式的几何线索来训练模型，取得了在几何和外观方面超过以前 3D 和关节感知方法的性能，通过系统的消融研究验证了模型的有效性和每个组件的重要性。