We present a new self-supervised approach, SelfPose3d, for estimating 3d
poses of multiple persons from multiple camera views. Unlike current
state-of-the-art fully-supervised methods, our approach does not require any 2d
or 3d ground-truth poses and uses only the multi-view input images from a
calibrated camera setup and 2d pseudo poses generated from an off-the-shelf 2d
human pose estimator. We propose two self-supervised learning objectives:
self-supervised person localization in 3d space and self-supervised 3d pose
estimation. We achieve self-supervised 3d person localization by training the
model on synthetically generated 3d points, serving as 3d person root
positions, and on the projected root-heatmaps in all the views. We then model
the 3d poses of all the localized persons with a bottleneck representation, map
them onto all views obtaining 2d joints, and render them using 2d Gaussian
heatmaps in an end-to-end differentiable manner. Afterwards, we use the
corresponding 2d joints and heatmaps from the pseudo 2d poses for learning. To
alleviate the intrinsic inaccuracy of the pseudo labels, we propose an adaptive
supervision attention mechanism to guide the self-supervision. Our experiments
and analysis on three public benchmark datasets, including Panoptic, Shelf, and
Campus, show the effectiveness of our approach, which is comparable to
fully-supervised methods. Code is available at
https://github.com/CAMMA-public/SelfPose3D

自我监督方法通过多视角输入图像和伪造姿势进行 3D 姿势估计，不需要 2D 或 3D 真实姿势标签，并且其有效性与完全监督方法相当。

SelfPose3d：自监督的多人多视角 3D 姿势估计

SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation

We present a method for generating high-quality watertight manifold meshes
from multi-view input images. Existing volumetric rendering methods are robust
in optimization but tend to generate noisy meshes with poor topology.
Differentiable rasterization-based methods can generate high-quality meshes but
are sensitive to initialization. Our method combines the benefits of both
worlds; we take the geometry initialization obtained from neural volumetric
fields, and further optimize the geometry as well as a compact neural texture
representation with differentiable rasterizers. Through extensive experiments,
we demonstrate that our method can generate accurate mesh reconstructions with
faithful appearance that are comparable to previous volume rendering methods
while being an order of magnitude faster in rendering. We also show that our
generated mesh and neural texture reconstruction is compatible with existing
graphics pipelines and enables downstream 3D applications such as simulation.
Project page: this https URL

本文介绍了一种从多视角输入图像生成高质量的水密流形网格的方法，该方法将神经体分布的几何初始化与可微分光栅化器进一步优化的不同优点结合起来，证明了该方法可以最大程度地准确重建网格并与现有的图形管道兼容，实现了类似模拟的 3D 应用。