End-to-end Recovery of Human Shape and Pose

TL;DR

This paper introduces Human Mesh Recovery (HMR), an end-to-end deep learning framework that reconstructs detailed 3D human body meshes from single RGB images, trained with minimal supervision and capable of real-time inference.

Contribution

It presents a novel end-to-end approach for 3D human mesh reconstruction from images, utilizing adversarial training and no reliance on intermediate keypoint detection.

Findings

Outperforms previous optimization-based methods in 3D mesh reconstruction

Operates in real-time from bounding boxes

Achieves competitive results in 3D joint estimation and segmentation

Abstract

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 images in-the-wild that only have ground truth 2D annotations. However, the reprojection loss alone leaves the model 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 paired 2D-to-3D supervision. We do not rely on intermediate 2D keypoint detections 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 optimization based methods that output 3D meshes and show competitive results on tasks such as 3D joint location estimation and part segmentation.