Monocular 3D human pose and shape estimation is an ill-posed problem since
multiple 3D solutions can explain a 2D image of a subject. Recent approaches
predict a probability distribution over plausible 3D pose and shape parameters
conditioned on the image. We show that these approaches exhibit a trade-off
between three key properties: (i) accuracy - the likelihood of the ground-truth
3D solution under the predicted distribution, (ii) sample-input consistency -
the extent to which 3D samples from the predicted distribution match the
visible 2D image evidence, and (iii) sample diversity - the range of plausible
3D solutions modelled by the predicted distribution. Our method, HuManiFlow,
predicts simultaneously accurate, consistent and diverse distributions. We use
the human kinematic tree to factorise full body pose into ancestor-conditioned
per-body-part pose distributions in an autoregressive manner. Per-body-part
distributions are implemented using normalising flows that respect the manifold
structure of SO(3), the Lie group of per-body-part poses. We show that
ill-posed, but ubiquitous, 3D point estimate losses reduce sample diversity,
and employ only probabilistic training losses. Code is available at:
this https URL

本文介绍了一种名为 HuManiFlow 的方法，利用概率分布和正规化流预测 3D 人体姿态和形状，同时具备准确性、一致性和多样性，解决了估计不明确的问题。

HuManiFlow: 在 SO (3) 流形上，对祖先进行调整的正则化流，用于人类姿势和形状分布估计

HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds  for Human Pose and Shape Distribution Estimation

Monocular 3D human pose and shape estimation is challenging due to the many
degrees of freedom of the human body and thedifficulty to acquire training data
for large-scale supervised learning in complex visual scenes. In this paper we
present practical semi-supervised and self-supervised models that support
training and good generalization in real-world images and video. Our
formulation is based on kinematic latent normalizing flow representations and
dynamics, as well as differentiable, semantic body part alignment loss
functions that support self-supervised learning. In extensive experiments using
3D motion capture datasets like CMU, Human3.6M, 3DPW, or AMASS, as well as
image repositories like COCO, we show that the proposed methods outperform the
state of the art, supporting the practical construction of an accurate family
of models based on large-scale training with diverse and incompletely labeled
image and video data.

本文提出了一种基于运动捕捉数据集和大规模图像和视频数据集的半监督和自监督学习方法，利用运动和不同 iable 的语义身体部位对齐损失函数进行训练，来精确地估计单眼三维人体姿态和形状。实验结果证明，该方法优于现有技术，可用于广泛的真实场景。