论文标题
3D人姿势估计的高级基线：两阶段方法
Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach
论文作者
论文摘要
人类姿势估计已广泛应用于各个行业。尽管最近几十年见证了许多先进的二维（2D）人类姿势估计解决方案，但三维（3D）人类姿势估计仍然是计算机视觉中的积极研究领域。一般而言，3D人类姿势估计方法可以分为两类：单阶段和两个阶段。在本文中，我们专注于两阶段方法中的2到3D提升过程，并根据现有解决方案提出了一个更先进的基线模型，以供3D人类姿势估计。我们的改进包括优化机器学习模型和多个参数，以及对培训模型的加权损失。最后，我们使用人为360万基准测试最终性能，并且确实产生了令人满意的结果。
Human pose estimation has been widely applied in various industries. While recent decades have witnessed the introduction of many advanced two-dimensional (2D) human pose estimation solutions, three-dimensional (3D) human pose estimation is still an active research field in computer vision. Generally speaking, 3D human pose estimation methods can be divided into two categories: single-stage and two-stage. In this paper, we focused on the 2D-to-3D lifting process in the two-stage methods and proposed a more advanced baseline model for 3D human pose estimation, based on the existing solutions. Our improvements include optimization of machine learning models and multiple parameters, as well as introduction of a weighted loss to the training model. Finally, we used the Human3.6M benchmark to test the final performance and it did produce satisfactory results.
