Recovering multi-person 3D poses with absolute scales from a single RGB image
is a challenging problem due to the inherent depth and scale ambiguity from a
single view. Addressing this ambiguity requires to aggregate various cues over
the entire image, such as body sizes, scene layouts, and inter-person
relationships. However, most previous methods adopt a top-down scheme that
first performs 2D pose detection and then regresses the 3D pose and scale for
each detected person individually, ignoring global contextual cues. In this
paper, we propose a novel system that first regresses a set of 2.5D
representations of body parts and then reconstructs the 3D absolute poses based
on these 2.5D representations with a depth-aware part association algorithm.
Such a single-shot bottom-up scheme allows the system to better learn and
reason about the inter-person depth relationship, improving both 3D and 2D pose
estimation. The experiments demonstrate that the proposed approach achieves the
state-of-the-art performance on the CMU Panoptic and MuPoTS-3D datasets and is
applicable to in-the-wild videos.

使用 2.5D 表示法并通过深度感知的算法进行部分关联，实现了单视图下从 RGB 图像中恢复出多人具有绝对尺度的 3D 姿态，该方法在 CMU Panoptic 和 MuPoTS-3D 数据集上达到了最新的技术水平，适用于野外视频。

SMAP: 单镜头多人绝对三维姿态估计

SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation

From a single image, humans are able to perceive the full 3D shape of an
object by exploiting learned shape priors from everyday life. Contemporary
single-image 3D reconstruction algorithms aim to solve this task in a similar
fashion, but often end up with priors that are highly biased by training
classes. Here we present an algorithm, Generalizable Reconstruction (GenRe),
designed to capture more generic, class-agnostic shape priors. We achieve this
with an inference network and training procedure that combine 2.5D
representations of visible surfaces (depth and silhouette), spherical shape
representations of both visible and non-visible surfaces, and 3D voxel-based
representations, in a principled manner that exploits the causal structure of
how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe
performs well on single-view shape reconstruction, and generalizes to diverse
novel objects from categories not seen during training.

本研究提出一种通用重建算法（GenRe），利用深度和轮廓等 2.5D 表示方法和球形和体素表示方法，旨在捕捉更通用，不受特定类别约束的形状先验知识，实验结果表明该算法可以很好地解决从单张图像还原 3D 形状，并且能够泛化到未在训练中见过的多种不同类别的新物体。