Generative models, like GANs [19], can be trained from
large image collections, to produce photo-realistic images
of objects [5, 29–31] and even clothed humans [2, 18, 20,
33, 34, 55]. The output, however, is only a 2D image and
many applications require diverse, high-quality, virtual 3D
avatars, with the ability to control poses and camera view-
points, while ensuring 3D consistency. To enable the gener-
ation of 3D avatars, the research community has been study-
ing generative models that can automatically produce 3D
shapes of humans and/or clothing based on input parame-
ters such as body pose and shape [9, 11, 38, 50]. Despite
rapid progress, most existing methods do not yet consider
texture and require accurate and clean 3D scans of humans
for training, which are expensive to acquire and hence lim-
ited in quantity and diversity. In this paper, we develop a
method that learns a generative model of 3D humans with
texture from only a set of unstructured 2D images of vari-
AG3D- Learning to Generate 3D Avatars from 2D Image Collections