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

Burst denoising is now more relevant than ever, as computational photography
helps overcome sensitivity issues inherent in mobile phones and small cameras.
A major challenge in burst-denoising is in coping with pixel misalignment,
which was so far handled with rather simplistic assumptions of simple motion,
or the ability to align in pre-processing. Such assumptions are not realistic
in the presence of large motion and high levels of noise. We show that Neural
Radiance Fields (NeRFs), originally suggested for physics-based novel-view
rendering, can serve as a powerful framework for burst denoising. NeRFs have an
inherent capability of handling noise as they integrate information from
multiple images, but they are limited in doing so, mainly since they build on
pixel-wise operations which are suitable to ideal imaging conditions. Our
approach, termed NAN, leverages inter-view and spatial information in NeRFs to
better deal with noise. It achieves state-of-the-art results in burst denoising
and is especially successful in coping with large movement and occlusions,
under very high levels of noise. With the rapid advances in accelerating NeRFs,
it could provide a powerful platform for denoising in challenging environments.

该研究使用神经辐射场方法处理像素错位和噪声问题来提升拍摄设备的图像质量和降噪能力。