Multi-view 3D human pose estimation is naturally superior to single view one,
benefiting from more comprehensive information provided by images of multiple
views. The information includes camera poses, 2D/3D human poses, and 3D
geometry. However, the accurate annotation of these information is hard to
obtain, making it challenging to predict accurate 3D human pose from multi-view
images. To deal with this issue, we propose a fully self-supervised framework,
named cascaded multi-view aggregating network (CMANet), to construct a
canonical parameter space to holistically integrate and exploit multi-view
information. In our framework, the multi-view information is grouped into two
categories: 1) intra-view information , 2) inter-view information. Accordingly,
CMANet consists of two components: intra-view module (IRV) and inter-view
module (IEV). IRV is used for extracting initial camera pose and 3D human pose
of each view; IEV is to fuse complementary pose information and cross-view 3D
geometry for a final 3D human pose. To facilitate the aggregation of the intra-
and inter-view, we define a canonical parameter space, depicted by per-view
camera pose and human pose and shape parameters ($\theta$ and $\beta$) of SMPL
model, and propose a two-stage learning procedure. At first stage, IRV learns
to estimate camera pose and view-dependent 3D human pose supervised by
confident output of an off-the-shelf 2D keypoint detector. At second stage, IRV
is frozen and IEV further refines the camera pose and optimizes the 3D human
pose by implicitly encoding the cross-view complement and 3D geometry
constraint, achieved by jointly fitting predicted multi-view 2D keypoints. The
proposed framework, modules, and learning strategy are demonstrated to be
effective by comprehensive experiments and CMANet is superior to
state-of-the-art methods in extensive quantitative and qualitative analysis.

提出了一种名为 CMANet 的全自我监督框架，利用多视角信息来预测准确的多视角 3D 人体姿势，通过聚合视角内和视角间信息，构建一个规范化参数空间

自学习的规范空间用于多视角三维人体姿态估计

Self-learning Canonical Space for Multi-view 3D Human Pose Estimation

Stereo image super-resolution aims to improve the quality of high-resolution
stereo image pairs by exploiting complementary information across views. To
attain superior performance, many methods have prioritized designing complex
modules to fuse similar information across views, yet overlooking the
importance of intra-view information for high-resolution reconstruction. It
also leads to problems of wrong texture in recovered images. To address this
issue, we explore the interdependencies between various hierarchies from
intra-view and propose a novel method, named Cross-View-Hierarchy Network for
Stereo Image Super-Resolution (CVHSSR). Specifically, we design a
cross-hierarchy information mining block (CHIMB) that leverages channel
attention and large kernel convolution attention to extract both global and
local features from the intra-view, enabling the efficient restoration of
accurate texture details. Additionally, a cross-view interaction module (CVIM)
is proposed to fuse similar features from different views by utilizing
cross-view attention mechanisms, effectively adapting to the binocular scene.
Extensive experiments demonstrate the effectiveness of our method. CVHSSR
achieves the best stereo image super-resolution performance than other
state-of-the-art methods while using fewer parameters. The source code and
pre-trained models are available at this https URL

本研究提出了一种名为 Cross-View-Hierarchy Network 的新方法，其中包括了利用通道关注和大核卷积关注从 intra-view 中提取全局和局部特征的交叉层次信息挖掘块（CHIMB），以及通过利用跨视图注意机制融合不同视图的相似特征的交叉视图交互模块（CVIM），并证明了 该方法比其他最先进的方法具有更好的立体图像超分辨率性能，且使用的参数更少。