论文标题
重建NBA球员
Reconstructing NBA Players
论文作者
论文摘要
从一张照片中以3D身体姿势和形状估计取得了巨大进展。然而，由于具有挑战性的身体姿势，建模衣服和自我阻塞，最先进的结果仍然遭受错误。篮球比赛的领域尤其具有挑战性，因为它表现出所有这些挑战。在本文中，我们介绍了一种重建篮球运动员的新方法，以优于最先进的方法。我们方法的关键是一种新的方法，用于创建可俯冲，皮肤的NBA玩家模型，以及一个大型网格数据库（源自NBA2K19视频游戏），我们正在向研究社区发布。基于这些型号，我们引入了一种新方法，该方法以任何篮球姿势输入一张衣服球员的照片，并为该玩家输出高分辨率网格和3D姿势。我们证明了对身体形状重建的最先进的单像方法的实质性改进。
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.
