论文标题
姿势：3D人类姿势估计具有新颖的人类姿势发生器和无偏学习
PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning
论文作者
论文摘要
3D姿势估计最近在计算机视觉领域中获得了重大利益。现有的3D姿势估计方法非常依赖大尺寸良好的3D姿势数据集，并且由于训练集中的3D姿势的有限多样性有限，它们在看不见的姿势上的模型概括不佳。在这项工作中，我们提出了一种新型的人类姿势发生器Posegu，该姿势生成多种姿势，只能访问少量的种子样本，同时为反事实风险最小化，以追求无偏见的评估目标。广泛的实验表明，在三个流行的基准数据集上，几乎所有正在考虑的最先进的3D人类姿势方法都超出了所有最新的3D人类姿势方法。经验分析还证明，姿势可以产生3D姿势，具有改进的数据多样性和更好的概括能力。
3D pose estimation has recently gained substantial interests in computer vision domain. Existing 3D pose estimation methods have a strong reliance on large size well-annotated 3D pose datasets, and they suffer poor model generalization on unseen poses due to limited diversity of 3D poses in training sets. In this work, we propose PoseGU, a novel human pose generator that generates diverse poses with access only to a small size of seed samples, while equipping the Counterfactual Risk Minimization to pursue an unbiased evaluation objective. Extensive experiments demonstrate PoseGU outforms almost all the state-of-the-art 3D human pose methods under consideration over three popular benchmark datasets. Empirical analysis also proves PoseGU generates 3D poses with improved data diversity and better generalization ability.
