  Great progress has been made in 3D body pose and shape estimation from a
single photo. Yet, state-of-the-art results still suffer from errors due to
challenging body poses, modeling clothing, and self occlusions. The domain of
basketball games is particularly challenging, as it exhibits all of these
challenges. In this paper, we introduce a new approach for reconstruction of
basketball players that outperforms the state-of-the-art. Key to our approach
is a new method for creating poseable, skinned models of NBA players, and a
large database of meshes (derived from the NBA2K19 video game), that we are
releasing to the research community. Based on these models, we introduce a new
method that takes as input a single photo of a clothed player in any basketball
pose and outputs a high resolution mesh and 3D pose for that player. We
demonstrate substantial improvement over state-of-the-art, single-image methods
for body shape reconstruction.
