We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that compute 2D or 3D joint locations, we produce a richer and more useful mesh representation that is parameterized by shape and 3D joint angles. The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using in-the-wild images that only have ground truth 2D annotations. However, reprojection loss alone is highly under constrained. In this work we address this problem by introducing an adversary trained to tell whether a human body parameter is real or not using a large database of 3D human meshes. We show that HMR can be trained with and without using any coupled 2D-to-3D supervision. We do not rely on intermediate 2D keypoint detection and infer 3D pose and shape parameters directly from image pixels. Our model runs in real-time given a bounding box containing the person. We demonstrate our approach on various images in-the-wild and out-perform previous optimizationbased methods that output 3D meshes and show competitive results on tasks such as 3D joint location estimation and part segmentation.

本文提出了一种名为HMR的端到端框架，可从单个RGB图像恢复人体的完整3D网格，通过优化关键点的后投影损失来训练模型，并使用对抗神经网络解决了模型参数欠约束的问题，最终实现了在野外图像中直接从像素预测人体姿态和形状参数的目标。

人体形状和姿势的端到端恢复