The goal of 2D human pose estimation (HPE) is to localize anatomical
landmarks, given an image of a person in a pose. SOTA techniques make use of
thousands of labeled figures (finetuning transformers or training deep CNNs),
acquired using labor-intensive crowdsourcing. On the other hand,
self-supervised methods re-frame the HPE task as a reconstruction problem,
enabling them to leverage the vast amount of unlabeled visual data, though at
the present cost of accuracy. In this work, we explore ways to improve
self-supervised HPE. We (1) analyze the relationship between reconstruction
quality and pose estimation accuracy, (2) develop a model pipeline that
outperforms the baseline which inspired our work, using less than one-third the
amount of training data, and (3) offer a new metric suitable for
self-supervised settings that measures the consistency of predicted body part
length proportions. We show that a combination of well-engineered
reconstruction losses and inductive priors can help coordinate pose learning
alongside reconstruction in a self-supervised paradigm.

简化中文摘要：本文探讨了改善自监督人体姿势估计的方法，包括分析重建质量与姿势估计准确性的关系，提出了一个优于基准模型的模型流水线，使用不到三分之一的训练数据量，并提出了一种适用于自监督环境的度量，用于衡量预测身体部位长度比例的一致性。结果表明，在自监督范式中，精心设计的重建损失和归纳假设可以帮助协调姿势估计和重建。

高效的自监督人体姿势估计与归纳先验调整

Efficient, Self-Supervised Human Pose Estimation with Inductive Prior  Tuning

Unsupervised depth completion methods are trained by minimizing sparse depth
and image reconstruction error. Block artifacts from resampling, intensity
saturation, and occlusions are amongst the many undesirable by-products of
common data augmentation schemes that affect image reconstruction quality, and
thus the training signal. Hence, typical augmentations on images that are
viewed as essential to training pipelines in other vision tasks have seen
limited use beyond small image intensity changes and flipping. The sparse depth
modality have seen even less as intensity transformations alter the scale of
the 3D scene, and geometric transformations may decimate the sparse points
during resampling. We propose a method that unlocks a wide range of
previously-infeasible geometric augmentations for unsupervised depth
completion. This is achieved by reversing, or "undo"-ing, geometric
transformations to the coordinates of the output depth, warping the depth map
back to the original reference frame. This enables computing the reconstruction
losses using the original images and sparse depth maps, eliminating the
pitfalls of naive loss computation on the augmented inputs. This simple yet
effective strategy allows us to scale up augmentations to boost performance. We
demonstrate our method on indoor (VOID) and outdoor (KITTI) datasets where we
improve upon three existing methods by an average of 10.4\% across both
datasets.

通过反向几何变换来进行无监督深度完成，解决了数据增强对图像重建质量的影响问题，从而实现了性能的提升。