While 3D body reconstruction methods have made remarkable progress recently,
it remains difficult to acquire the sufficiently accurate and numerous 3D
supervisions required for training. In this paper, we propose \textbf{KNOWN}, a
framework that effectively utilizes body \textbf{KNOW}ledge and
u\textbf{N}certainty modeling to compensate for insufficient 3D supervisions.
KNOWN exploits a comprehensive set of generic body constraints derived from
well-established body knowledge. These generic constraints precisely and
explicitly characterize the reconstruction plausibility and enable 3D
reconstruction models to be trained without any 3D data. Moreover, existing
methods typically use images from multiple datasets during training, which can
result in data noise (\textit{e.g.}, inconsistent joint annotation) and data
imbalance (\textit{e.g.}, minority images representing unusual poses or
captured from challenging camera views). KNOWN solves these problems through a
novel probabilistic framework that models both aleatoric and epistemic
uncertainty. Aleatoric uncertainty is encoded in a robust Negative
Log-Likelihood (NLL) training loss, while epistemic uncertainty is used to
guide model refinement. Experiments demonstrate that KNOWN's body
reconstruction outperforms prior weakly-supervised approaches, particularly on
the challenging minority images.

利用已知知识和不确定性建模以弥补不足的三维监督的 3D 人体重建方法

单目三维人体重建的身体知识和不确定性建模

Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body  Reconstruction

Reconstructing 3D human body shapes from 3D partial textured scans remains a
fundamental task for many computer vision and graphics applications -- e.g.,
body animation, and virtual dressing. We propose a new neural network
architecture for 3D body shape and high-resolution texture completion --
BCom-Net -- that can reconstruct the full geometry from mid-level to high-level
partial input scans. We decompose the overall reconstruction task into two
stages - first, a joint implicit learning network (SCom-Net and TCom-Net) that
takes a voxelized scan and its occupancy grid as input to reconstruct the full
body shape and predict vertex textures. Second, a high-resolution texture
completion network, that utilizes the predicted coarse vertex textures to
inpaint the missing parts of the partial 'texture atlas'. A thorough
experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves
competitive results with respect to the state-of-the-art while generalizing to
different types and levels of partial shapes. The proposed method has also
ranked second in the track1 of SHApe Recovery from Partial textured 3D scans
(SHARP [38,1]) 2022 challenge1.

利用 BCom-Net 神经网络构建了一个两阶段的方法来实现从部分输入的 3D 扫描中完整地重建出身体形态和纹理，并在 SHARP 2022 挑战中获得了第二名。

TSCom-Net: 粗到细的三维纹理形状补全网络

TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network

Although synthetic training data has been shown to be beneficial for tasks
such as human pose estimation, its use for RGB human action recognition is
relatively unexplored. Our goal in this work is to answer the question whether
synthetic humans can improve the performance of human action recognition, with
a particular focus on generalization to unseen viewpoints. We make use of the
recent advances in monocular 3D human body reconstruction from real action
sequences to automatically render synthetic training videos for the action
labels. We make the following contributions: (i) we investigate the extent of
variations and augmentations that are beneficial to improving performance at
new viewpoints. We consider changes in body shape and clothing for individuals,
as well as more action relevant augmentations such as non-uniform frame
sampling, and interpolating between the motion of individuals performing the
same action; (ii) We introduce a new data generation methodology, SURREACT,
that allows training of spatio-temporal CNNs for action classification; (iii)
We substantially improve the state-of-the-art action recognition performance on
the NTU RGB+D and UESTC standard human action multi-view benchmarks; Finally,
(iv) we extend the augmentation approach to in-the-wild videos from a subset of
the Kinetics dataset to investigate the case when only one-shot training data
is available, and demonstrate improvements in this case as well.

本研究旨在探讨合成人类能否提高人类动作识别的性能，特别关注对不寻常拍摄视角的泛化能力，研究发现数据的合成和扩充可以带来性能的提升。