论文标题
多人3D姿势和形状估计通过逆运动学和改进
Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement
论文作者
论文摘要
以单眼RGB图像的网格形式估算3D姿势和形状是具有挑战性的。显然，这比仅以骨骼或热图的形式估算3D姿势要困难。当涉及互动的人时，由于人与人的阻塞引起了歧义，3D网状重建变得更具挑战性。为了应对挑战，我们提出了一条粗到精细的管道，该管道受益于1）从闭塞3D骨架估计和2）基于变压器的关系感知的改进技术中受益于1）逆运动学。在我们的管道中，我们首先从RGB图像中获得了多个人的闭塞3D骨架。然后，我们应用逆运动学将估计的骨骼转换为可变形的3D网格参数。最后，我们应用了基于变压器的网格细化，该细化构成了考虑3D网格内部和人际关系关系的获得的网格参数。通过广泛的实验，我们证明了方法的有效性，在3DPW，mupots和agora数据集上表现出色。
Estimating 3D poses and shapes in the form of meshes from monocular RGB images is challenging. Obviously, it is more difficult than estimating 3D poses only in the form of skeletons or heatmaps. When interacting persons are involved, the 3D mesh reconstruction becomes more challenging due to the ambiguity introduced by person-to-person occlusions. To tackle the challenges, we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics from the occlusion-robust 3D skeleton estimation and 2) Transformer-based relation-aware refinement techniques. In our pipeline, we first obtain occlusion-robust 3D skeletons for multiple persons from an RGB image. Then, we apply inverse kinematics to convert the estimated skeletons to deformable 3D mesh parameters. Finally, we apply the Transformer-based mesh refinement that refines the obtained mesh parameters considering intra- and inter-person relations of 3D meshes. Via extensive experiments, we demonstrate the effectiveness of our method, outperforming state-of-the-arts on 3DPW, MuPoTS and AGORA datasets.
