3D Human Pose Estimation = 2D Pose Estimation + Matching

We explore 3D human pose estimation from a single RGB image. While many approaches try to directly predict 3D pose from image measurements, we explore a simple architecture that reasons through intermediate 2D pose predictions. Our approach is based on two key observations (1) Deep neural nets have revolutionized 2D pose estimation, producing accurate 2D predictions even for poses with self-occlusions (2) Big-datasets of 3D mocap data are now readily available, making it tempting to lift predicted 2D poses to 3D through simple memorization (e.g., nearest neighbors). The resulting architecture is straightforward to implement with off-the-shelf 2D pose estimation systems and 3D mocap libraries. Importantly, we demonstratethatsuchmethodsoutperformalmostallstate-of-theart 3D pose estimation systems, most of which directly try to regress 3D pose from 2D measurements.

1. Introduction

Inferring 3D human pose from image measurements is classic task in computer vision, dating back to the iconic work of Hogg [11] and O'Rourke and Badler [22]. Such a technology has immediate applications in various tasks such as action understanding, surveillance, human-robot interaction, and motion capture, to name a few. As such, it has a long and storied history. We refer the reader to various surveys for a broad overview of the popular topic [8], [19].