High-resolution representation is essential for achieving good performance in
human pose estimation models. To obtain such features, existing works utilize
high-resolution input images or fine-grained image tokens. However, this dense
high-resolution representation brings a significant computational burden. In
this paper, we address the following question: "Only sparse human keypoint
locations are detected for human pose estimation, is it really necessary to
describe the whole image in a dense, high-resolution manner?" Based on dynamic
transformer models, we propose a framework that only uses Sparse
High-resolution Representations for human Pose estimation (SHaRPose). In
detail, SHaRPose consists of two stages. At the coarse stage, the relations
between image regions and keypoints are dynamically mined while a coarse
estimation is generated. Then, a quality predictor is applied to decide whether
the coarse estimation results should be refined. At the fine stage, SHaRPose
builds sparse high-resolution representations only on the regions related to
the keypoints and provides refined high-precision human pose estimations.
Extensive experiments demonstrate the outstanding performance of the proposed
method. Specifically, compared to the state-of-the-art method ViTPose, our
model SHaRPose-Base achieves 77.4 AP (+0.5 AP) on the COCO validation set and
76.7 AP (+0.5 AP) on the COCO test-dev set, and infers at a speed of
$1.4\times$ faster than ViTPose-Base.

提出了使用动态转换模型的稀疏高分辨率表示来进行人体姿势估计的框架 SHaRPose，并通过实验证明了其优越性能。

SHaRPose：稀疏高分辨率人体姿态估计

SHaRPose: Sparse High-Resolution Representation for Human Pose  Estimation

High-resolution representation is necessary for human pose estimation to
achieve high performance, and the ensuing problem is high computational
complexity. In particular, predominant pose estimation methods estimate human
joints by 2D single-peak heatmaps. Each 2D heatmap can be horizontally and
vertically projected to and reconstructed by a pair of 1D heat vectors.
Inspired by this observation, we introduce a lightweight and powerful
alternative, Spatially Unidimensional Self-Attention (SUSA), to the pointwise
(1x1) convolution that is the main computational bottleneck in the depthwise
separable 3c3 convolution. Our SUSA reduces the computational complexity of the
pointwise (1x1) convolution by 96% without sacrificing accuracy. Furthermore,
we use the SUSA as the main module to build our lightweight pose estimation
backbone X-HRNet, where `X' represents the estimated cross-shape attention
vectors. Extensive experiments on the COCO benchmark demonstrate the
superiority of our X-HRNet, and comprehensive ablation studies show the
effectiveness of the SUSA modules. The code is publicly available at
this https URL

通过引入 Spatially Unidimensional Self-Attention (SUSA)，我们减少了 pointwise（1x1）卷积的计算复杂度，并构建了轻量级姿势估计框架 X-HRNet，该框架在 COCO 基准测试中表现卓越，并且 SUSA 模块的有效性得到了全面验证。

X-HRNet: 基于空间一维自注意力的轻量级人体姿态估计

X-HRNet: Towards Lightweight Human Pose Estimation with Spatially  Unidimensional Self-Attention

We present AutoPose, a novel neural architecture search(NAS) framework that
is capable of automatically discovering multiple parallel branches of
cross-scale connections towards accurate and high-resolution 2D human pose
estimation. Recently, high-performance hand-crafted convolutional networks for
pose estimation show growing demands on multi-scale fusion and high-resolution
representations. However, current NAS works exhibit limited flexibility on
scale searching, they dominantly adopt simplified search spaces of
single-branch architectures. Such simplification limits the fusion of
information at different scales and fails to maintain high-resolution
representations. The presentedAutoPose framework is able to search for
multi-branch scales and network depth, in addition to the cell-level
microstructure. Motivated by the search space, a novel bi-level optimization
method is presented, where the network-level architecture is searched via
reinforcement learning, and the cell-level search is conducted by the
gradient-based method. Within 2.5 GPU days, AutoPose is able to find very
competitive architectures on the MS COCO dataset, that are also transferable to
the MPII dataset. Our code is available at
this https URL

AutoPose 是一种新颖的神经架构搜索（NAS）框架，它能够自动发现多个跨尺度连接的平行分支，以实现准确和高分辨率的 2D 人体姿态估计，通过搜索多分支尺度和网络深度，以及在细胞水平进行搜寻的方法，它在短短 2.5 GPU 天内，在 MS COCO 数据集上找到了竞争力非常强的架构，可传递到 MPII 数据集。

AutoPose: 多尺度分支聚合姿态估计搜索

AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation

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。