2019.8
论文地址：https://arxiv.org/pdf/1908.09220.pdf
Abstract
Multi-person pose estimation is a challenging problem. Existing methods are mostly two-stage based—one stage for proposal generation and the other for allocating poses to corresponding persons. However, such two-stage methods generally suffer low efficiency. In this work, we present the first single-stage model, Single-stage multi-person Pose Machine (SPM), to simplify the pipeline and lift the efficiency for multi-person pose estimation. To achieve this, we propose a novel Structured Pose Representation (SPR) that unifies person instance and body joint position representations. Based on SPR, we develop the SPM model that can directly predict structured poses for multiple persons in a single stage, and thus offer a more compact pipeline and attractive efficiency advantage over two-stage methods. In particular, SPR introduces the root joints to indicate different person instances and human body joint positions are encoded into their displacements w.r.t. the roots. To better predict long-range displacements for some joints, SPR is further extended to hierarchical representations. Based on SPR, SPM can efficiently perform multi-person poses estimation by simultaneously predicting root joints (location of instances) and body joint displacements via CNNs. Moreover, to demonstrate the generality of SPM, we also apply it to multi-person 3D pose estimation. Comprehensive experiments on benchmarks MPII, extended PASCAL-PersonPart, MSCOCO and CMU Panoptic clearly demonstrate the state-of-the-art efficiency of SPM for multi-person 2D/3D pose estimation, together with outstanding accuracy.
多人姿势估计是一个具有挑战性的问题。现有方法大多是基于两阶段的 - 一个用于提议生成的阶段，另一个用于向相应的人分配姿势。然而，这种两阶段方法通常效率低。在这项工作中，我们提出了第一个单阶段模型，单阶段多人姿势机（SPM），以简化管道和提高多人姿势估计的效率。为实现这一目标，我们提出了一种新颖的结构化姿势表示（SPR），它统一了人体实例和身体关节位置表示。基于SPR，我们开发了SPM模型，可以在一个阶段直接预测多人的结构化姿势，从而提供比两阶段方法更紧凑的管道和有吸引力的效率优势。特别地，SPR引入根关节以指示不同的人物实例，并且人体关节位置被编码到它们的位移w.r.t.根。为了更好地预测某些关节的长程位移，SPR进一步扩展到分层表示。基于SPR，SPM可以通过CNN同时预测根关节（实例的位置）和身体关节位移来有效地执行多人姿势估计。此外，为了证明SPM的一般性，我们还将其应用于多人3D姿态估计。基准MPII，扩展PASCAL-PersonPart，MSCOCO和CMU Panoptic的综合实验清楚地展示了SPM在多人2D / 3D姿态估计方面的最先进效率，以及出色的准确性。
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