Single-image 3D human reconstruction aims to reconstruct the 3D textured
surface of the human body given a single image. While implicit function-based
methods recently achieved reasonable reconstruction performance, they still
bear limitations showing degraded quality in both surface geometry and texture
from an unobserved view. In response, to generate a realistic textured surface,
we propose ReFu, a coarse-to-fine approach that refines the projected backside
view image and fuses the refined image to predict the final human body. To
suppress the diffused occupancy that causes noise in projection images and
reconstructed meshes, we propose to train occupancy probability by
simultaneously utilizing 2D and 3D supervisions with occupancy-based volume
rendering. We also introduce a refinement architecture that generates
detail-preserving backside-view images with front-to-back warping. Extensive
experiments demonstrate that our method achieves state-of-the-art performance
in 3D human reconstruction from a single image, showing enhanced geometry and
texture quality from an unobserved view.

使用 ReFu 方法，结合 2D 和 3D 监督，利用减少扩散占用概率的体积渲染，生成具有细节保留的背面图像，并将经过修整的图像与前端的反向变形融合以预测出人体模型，最终在单张图像 3D 人体重建中取得了最新成就。

ReFu: 通过细节保留的单张照片三维人体建模，对未观察的视角进行优化和融合

ReFu: Refine and Fuse the Unobserved View for Detail-Preserving  Single-Image 3D Human Reconstruction

This paper describes a novel lossless point cloud compression algorithm that
uses a neural network for estimating the coding probabilities for the occupancy
status of voxels, depending on wide three dimensional contexts around the voxel
to be encoded. The point cloud is represented as an octree, with each
resolution layer being sequentially encoded and decoded using arithmetic
coding, starting from the lowest resolution, until the final resolution is
reached. The occupancy probability of each voxel of the splitting pattern at
each node of the octree is modeled by a neural network, having at its input the
already encoded occupancy status of several octree nodes (belonging to the past
and current resolutions), corresponding to a 3D context surrounding the node to
be encoded. The algorithm has a fast and a slow version, the fast version
selecting differently several voxels of the context, which allows an increased
parallelization by sending larger batches of templates to be estimated by the
neural network, at both encoder and decoder. The proposed algorithms yield
state-of-the-art results on benchmark datasets. The implementation will be made
available at this https URL

本文提出了一种新型的无损点云压缩算法，采用神经网络来估计体素的编码概率，通过表示为八叉树，以从低分辨率开始，逐层编解码的方式来占据状态，并利用神经网络对占据状态进行建模，具有快速和慢速版本，并在基准数据集上取得了最新成果。