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.
翻译:虽然近几十年来,人们采用了许多先进的二维(2D)人类构成估计方法,但三维(3D)人类构成估计仍然是计算机愿景的一个积极的研究领域,一般来说,3D人类构成估计方法可以分为两类:一阶段和两阶段。在本文件中,我们侧重于两阶段方法中的2D-3D提升过程,并根据现有解决方案为3D人类构成估计提出了一个更先进的基线模型。我们的改进包括优化机器学习模型和多重参数,以及将加权损失引入培训模型。最后,我们用人文3.6M基准测试了最后的绩效,并取得了令人满意的结果。