Reconstructing NBA Players

Luyang Zhu, Konstantinos Rematas, Brian Curless, Steven M. Seitz, Ira Kemelmacher-Shlizerman ;

Great progress has been made in 3D body pose and shape estimation from single photos. 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, due to all of these factors. 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 new approach 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 performing any basketball pose and outputs a high resolution mesh and pose of that player. We compare to state of the art methods for shape generation and show significant improvement in the results. "