This is an official pytorch implementation of Deep High-Resolution
Representation Learning for Human Pose Estimation. In this work, we are
interested in the human pose estimation problem with a focus on learning
reliable high-resolution representations. Most existing methods recover
high-resolution representations from low-resolution representations produced by
a high-to-low resolution network. Instead, our proposed network maintains
high-resolution representations through the whole process. We start from a
high-resolution subnetwork as the first stage, gradually add high-to-low
resolution subnetworks one by one to form more stages, and connect the
mutli-resolution subnetworks in parallel. We conduct repeated multi-scale
fusions such that each of the high-to-low resolution representations receives
information from other parallel representations over and over, leading to rich
high-resolution representations. As a result, the predicted keypoint heatmap is
potentially more accurate and spatially more precise. We empirically
demonstrate the effectiveness of our network through the superior pose
estimation results over two benchmark datasets: the COCO keypoint detection
dataset and the MPII Human Pose dataset. The code and models have been publicly
available at
https://github.com/leoxiaobin/deep-high-resolution-net.pytorch.

本论文利用 pytorch 实现了一种深度高分辨率表示学习方法用于人体姿态估计，通过建立多分辨率子网并在并行连接中进行信息融合，最终得到更精准、更高精度的关键点热图，实验证明该方法在 COCO 关键点检测数据集和 MPII 人体姿态数据集上具有显著优势。

深度高分辨率表示学习人体姿态估计

Deep High-Resolution Representation Learning for Human Pose Estimation

Understanding 3D object structure from a single image is an important but
difficult task in computer vision, mostly due to the lack of 3D object
annotations in real images. Previous work tackles this problem by either
solving an optimization task given 2D keypoint positions, or training on
synthetic data with ground truth 3D information. In this work, we propose 3D
INterpreter Network (3D-INN), an end-to-end framework which sequentially
estimates 2D keypoint heatmaps and 3D object structure, trained on both real
2D-annotated images and synthetic 3D data. This is made possible mainly by two
technical innovations. First, we propose a Projection Layer, which projects
estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D
structural parameters supervised by 2D annotations on real images. Second,
heatmaps of keypoints serve as an intermediate representation connecting real
and synthetic data, enabling 3D-INN to benefit from the variation and abundance
of synthetic 3D objects, without suffering from the difference between the
statistics of real and synthesized images due to imperfect rendering. The
network achieves state-of-the-art performance on both 2D keypoint estimation
and 3D structure recovery. We also show that the recovered 3D information can
be used in other vision applications, such as 3D rendering and image retrieval.

本文提出了一种名为 3D Interpreter Network（3D-INN）的端到端框架，通过序贯估计 2D 关键点热图和 3D 物体结构，该网络主要依靠两项技术创新，即 Projection Layer 和关键点热图。通过在真实 2D 注释图像和合成 3D 数据上训练，3D-INN 在 2D 关键点估计和 3D 结构恢复上均取得了最先进的性能。