Reconstructing NBA Players
This work addresses the specific problem of reconstructing NBA players from single images for computer vision and graphics applications, but it is incremental as it builds on existing methods with domain-specific enhancements.
The paper tackles the problem of 3D body pose and shape estimation from a single photo in the challenging domain of basketball games, introducing a new approach that outperforms state-of-the-art methods with substantial improvement.
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.