3D face reconstruction is a challenging problem but also an important task in
the field of computer vision and graphics. Recently, many researchers put
attention to the problem and a large number of articles have been published.
Single image reconstruction is one of the branches of 3D face reconstruction,
which has a lot of applications in our life. This paper is a review of the
recent literature on 3D face reconstruction from a single image.

本文是一篇从单张图像出发对 3D 人脸重建的近期研究文献的综述。

从单张图像进行三维面部重建综述

A Review of 3D Face Reconstruction From a Single Image

Most consumer-grade digital cameras can only capture a limited range of
luminance in real-world scenes due to sensor constraints. Besides, noise and
quantization errors are often introduced in the imaging process. In order to
obtain high dynamic range (HDR) images with excellent visual quality, the most
common solution is to combine multiple images with different exposures.
However, it is not always feasible to obtain multiple images of the same scene
and most HDR reconstruction methods ignore the noise and quantization loss. In
this work, we propose a novel learning-based approach using a spatially dynamic
encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single
image HDR reconstruction with denoising and dequantization. The network
consists of a UNet-style base network to make full use of the hierarchical
multi-scale information, a condition network to perform pattern-specific
modulation and a weighting network for selectively retaining information.
Moreover, we propose a Tanh_L1 loss function to balance the impact of
over-exposed values and well-exposed values on the network learning. Our method
achieves the state-of-the-art performance in quantitative comparisons and
visual quality. The proposed HDRUNet model won the second place in the single
frame track of NITRE2021 High Dynamic Range Challenge.

本文提出了一种基于学习的方法，使用空间动态编码器 - 解码器网络，HDRUNet，通过去噪和去量化来实现单张图像的高动态范围重建，提出了 Tahn_L1 损失函数，获得了最先进的性能。

HDRUNet: 使用去噪和去量化技术进行单图高动态范围图像重建

HDRUNet: Single Image HDR Reconstruction with Denoising and  Dequantization

Recovering the 3D shape of a person from its 2D appearance is ill-posed due
to ambiguities. Nevertheless, with the help of convolutional neural networks
(CNN) and prior knowledge on the 3D human body, it is possible to overcome such
ambiguities to recover detailed 3D shapes of human bodies from single images.
Current solutions, however, fail to reconstruct all the details of a person
wearing loose clothes. This is because of either (a) huge memory requirement
that cannot be maintained even on modern GPUs or (b) the compact 3D
representation that cannot encode all the details. In this paper, we propose
the tetrahedral outer shell volumetric truncated signed distance function
(TetraTSDF) model for the human body, and its corresponding part connection
network (PCN) for 3D human body shape regression. Our proposed model is
compact, dense, accurate, and yet well suited for CNN-based regression task.
Our proposed PCN allows us to learn the distribution of the TSDF in the
tetrahedral volume from a single image in an end-to-end manner. Results show
that our proposed method allows to reconstruct detailed shapes of humans
wearing loose clothes from single RGB images.

该研究提出了一种基于 TetraTSDF 和 PCN 的人体建模方法，通过卷积神经网络和三维人体先验知识，可以从单张图像中重建穿着宽松衣服的真实 3D 人体模型。