We present a new end-to-end learning framework to obtain detailed and
spatially coherent reconstructions of multiple people from a single image.
Existing multi-person methods suffer from two main drawbacks: they are often
model-based and therefore cannot capture accurate 3D models of people with
loose clothing and hair; or they require manual intervention to resolve
occlusions or interactions. Our method addresses both limitations by
introducing the first end-to-end learning approach to perform model-free
implicit reconstruction for realistic 3D capture of multiple clothed people in
arbitrary poses (with occlusions) from a single image. Our network
simultaneously estimates the 3D geometry of each person and their 6DOF spatial
locations, to obtain a coherent multi-human reconstruction. In addition, we
introduce a new synthetic dataset that depicts images with a varying number of
inter-occluded humans and a variety of clothing and hair styles. We demonstrate
robust, high-resolution reconstructions on images of multiple humans with
complex occlusions, loose clothing and a large variety of poses and scenes. Our
quantitative evaluation on both synthetic and real-world datasets demonstrates
state-of-the-art performance with significant improvements in the accuracy and
completeness of the reconstructions over competing approaches.

本研究提出了一种新的端到端学习框架，可以从一张图像中获取多人的详细且具有空间连续性的重建，并能解决多人姿态、遮挡、衣着等问题带来的困难，实现了对穿衣人体在任意姿势下的模型自由_implcicit 3D 重建和同步估计。针对模型和数据集，实验结果表明，该方法与现有的竞争方法相比，具有更高的精度和完整性，呈现了最先进的性能。

单张图片中的多人隐式重建

Multi-person Implicit Reconstruction from a Single Image

We present a novel method to learn temporally consistent 3D reconstruction of
clothed people from a monocular video. Recent methods for 3D human
reconstruction from monocular video using volumetric, implicit or parametric
human shape models, produce per frame reconstructions giving temporally
inconsistent output and limited performance when applied to video. In this
paper, we introduce an approach to learn temporally consistent features for
textured reconstruction of clothed 3D human sequences from monocular video by
proposing two advances: a novel temporal consistency loss function; and hybrid
representation learning for implicit 3D reconstruction from 2D images and
coarse 3D geometry. The proposed advances improve the temporal consistency and
accuracy of both the 3D reconstruction and texture prediction from a monocular
video. Comprehensive comparative performance evaluation on images of people
demonstrates that the proposed method significantly outperforms the
state-of-the-art learning-based single image 3D human shape estimation
approaches achieving significant improvement of reconstruction accuracy,
completeness, quality and temporal consistency.

本文介绍了一种从单目视频中学习时态一致的 3D 服饰人物重建的新方法，该方法通过引入新的时间一致性损失函数和混合表示学习，显著提高了重建的准确性、完整性、质量和时间一致性。

使用时间一致性损失实现单目视频高分辨率纹理和服饰 3D 人模重建

Temporal Consistency Loss for High Resolution Textured and Clothed  3DHuman Reconstruction from Monocular Video

Three-dimensional human body models are widely used in the analysis of human
pose and motion. Existing models, however, are learned from minimally-clothed
3D scans and thus do not generalize to the complexity of dressed people in
common images and videos. Additionally, current models lack the expressive
power needed to represent the complex non-linear geometry of pose-dependent
clothing shapes. To address this, we learn a generative 3D mesh model of
clothed people from 3D scans with varying pose and clothing. Specifically, we
train a conditional Mesh-VAE-GAN to learn the clothing deformation from the
SMPL body model, making clothing an additional term in SMPL. Our model is
conditioned on both pose and clothing type, giving the ability to draw samples
of clothing to dress different body shapes in a variety of styles and poses. To
preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to
3D meshes. Our model, named CAPE, represents global shape and fine local
structure, effectively extending the SMPL body model to clothing. To our
knowledge, this is the first generative model that directly dresses 3D human
body meshes and generalizes to different poses. The model, code and data are
available for research purposes at this https URL

本文提出了一种名为 CAPE 的基于 Mesh-VAE-GAN 和 SMPL 身体模型的条件服装生成模型，能够直接为 3D 人体模型着装并适用于不同的姿势。