  The creation of 2D realistic facial images and 3D face shapes using
generative networks has been a hot topic in recent years. Existing face
generators exhibit exceptional performance on faces in small to medium poses
(with respect to frontal faces) but struggle to produce realistic results for
large poses. The distorted rendering results on large poses in 3D-aware
generators further show that the generated 3D face shapes are far from the
distribution of 3D faces in reality. We find that the above issues are caused
by the training dataset's pose imbalance.
  In this paper, we present LPFF, a large-pose Flickr face dataset comprised of
19,590 high-quality real large-pose portrait images. We utilize our dataset to
train a 2D face generator that can process large-pose face images, as well as a
3D-aware generator that can generate realistic human face geometry. To better
validate our pose-conditional 3D-aware generators, we develop a new FID measure
to evaluate the 3D-level performance. Through this novel FID measure and other
experiments, we show that LPFF can help 2D face generators extend their latent
space and better manipulate the large-pose data, and help 3D-aware face
generators achieve better view consistency and more realistic 3D reconstruction
results.
