We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints. To this end, we learn to estimate 3D positions of 26 body keypoints and propose an analytical solution to articulate a parametric body model, SMPL, via a set of straightforward geometric transformations. Since keypoint estimation directly relies on image clues, our approach offers significantly better alignment to image content when compared to state-of-the-art approaches. Our proposed approach does not require any paired mesh annotations and is able to achieve state-of-the-art mesh fittings through 3D keypoint regression only. Results on the challenging 3DPW and Human3.6M demonstrate that our approach yields state-of-the-art body mesh fittings.

翻译：我们展示了3D关键点认识网格分辨法KAMA,3D关键点是3D关键点的位置,使我们能够从3D关键点的位置中估计人体的网格。为此,我们学会了对26个正方关键点的3D位置进行估计,并通过一系列直截了当的几何转换,提出了阐明参数体模型SMPL的分析解决方案。由于关键点估计直接依赖于图像线索,我们的方法与最新方法相比,与图像内容的匹配程度要高得多。我们提出的方法不需要任何对齐网点说明,只能通过3D关键点回归实现最先进的网格装配。关于挑战性3DPW和Human3.6M的结果显示,我们的方法产生最先进的结构组合。