In this work, we address the problem of multi-person 3D pose estimation from
a single image. A typical regression approach in the top-down setting of this
problem would first detect all humans and then reconstruct each one of them
independently. However, this type of prediction suffers from incoherent
results, e.g., interpenetration and inconsistent depth ordering between the
people in the scene. Our goal is to train a single network that learns to avoid
these problems and generate a coherent 3D reconstruction of all the humans in
the scene. To this end, a key design choice is the incorporation of the SMPL
parametric body model in our top-down framework, which enables the use of two
novel losses. First, a distance field-based collision loss penalizes
interpenetration among the reconstructed people. Second, a depth ordering-aware
loss reasons about occlusions and promotes a depth ordering of people that
leads to a rendering which is consistent with the annotated instance
segmentation. This provides depth supervision signals to the network, even if
the image has no explicit 3D annotations. The experiments show that our
approach outperforms previous methods on standard 3D pose benchmarks, while our
proposed losses enable more coherent reconstruction in natural images. The
project website with videos, results, and code can be found at:
this https URL

本研究针对单张图像中的多人 3D 姿态估计问题，采用自顶向下的方法，并利用 SMPL 参数化身体模型，提出了基于距离场碰撞损失和深度排序感知损失的网络训练框架，有效解决了结果不连贯的问题，并且在标准的 3D 姿态基准测试中表现出优异性能。

从单张图像中连贯重建多人

Coherent Reconstruction of Multiple Humans from a Single Image

We present a learning-based method for representing grasp poses of a high-DOF
hand using neural networks. Due to redundancy in such high-DOF grippers, there
exists a large number of equally effective grasp poses for a given target
object, making it difficult for the neural network to find consistent grasp
poses. We resolve this ambiguity by generating an augmented dataset that covers
many possible grasps for each target object and train our neural networks using
a consistency loss function to identify a one-to-one mapping from objects to
grasp poses. We further enhance the quality of neural-network-predicted grasp
poses using a collision loss function to avoid penetrations. We use an object
dataset that combines the BigBIRD Database, the KIT Database, the YCB Database,
and the Grasp Dataset to show that our method can generate high-DOF grasp poses
with higher accuracy than supervised learning baselines. The quality of the
grasp poses is on par with the groundtruth poses in the dataset. In addition,
our method is robust and can handle noisy object models such as those
constructed from multi-view depth images, allowing our method to be implemented
on a 25-DOF Shadow Hand hardware platform.

本文提出了一种基于神经网络的学习方法，用于表示高自由度手的抓握姿势，并生成一个包含多种可能抓握的扩充数据集以避免模型的不确定性。该方法使用一致性损失函数和碰撞损失函数进行训练，在多个数据集上均取得了高精度，同时可以应用于噪声对象模型。