This paper addresses the problem of 3D human body shape and pose estimation from an RGB image. This is often an ill-posed problem, since multiple plausible 3D bodies may match the visual evidence present in the input - particularly when the subject is occluded. Thus, it is desirable to estimate a distribution over 3D body shape and pose conditioned on the input image instead of a single 3D reconstruction. We train a deep neural network to estimate a hierarchical matrix-Fisher distribution over relative 3D joint rotation matrices (i.e. body pose), which exploits the human body's kinematic tree structure, as well as a Gaussian distribution over SMPL body shape parameters. To further ensure that the predicted shape and pose distributions match the visual evidence in the input image, we implement a differentiable rejection sampler to impose a reprojection loss between ground-truth 2D joint coordinates and samples from the predicted distributions, projected onto the image plane. We show that our method is competitive with the state-of-the-art in terms of 3D shape and pose metrics on the SSP-3D and 3DPW datasets, while also yielding a structured probability distribution over 3D body shape and pose, with which we can meaningfully quantify prediction uncertainty and sample multiple plausible 3D reconstructions to explain a given input image. Code is available at https://github.com/akashsengupta1997/HierarchicalProbabilistic3DHuman .

通过训练深度神经网络，使用人体运动树结构来估计相对3D关节旋转矩阵的分层矩阵费舍尔分布（即身体姿态）和SMPL身体形状参数的高斯分布，实现了对3D人体形状和姿态的分布性预测，能够有效地量化预测不确定性并从中采样出多种可能的3D重建形式，从而解释给定的输入图像。

基于层级运动学概率分布的野外图像3D人体形状和姿态估计