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

We present an approach to infer the 3D shape, texture, and camera pose for an
object from a single RGB image, using only category-level image collections
with foreground masks as supervision. We represent the shape as an
image-conditioned implicit function that transforms the surface of a sphere to
that of the predicted mesh, while additionally predicting the corresponding
texture. To derive supervisory signal for learning, we enforce that: a) our
predictions when rendered should explain the available image evidence, and b)
the inferred 3D structure should be geometrically consistent with learned pixel
to surface mappings. We empirically show that our approach improves over prior
work that leverages similar supervision, and in fact performs competitively to
methods that use stronger supervision. Finally, as our method enables learning
with limited supervision, we qualitatively demonstrate its applicability over a
set of about 30 object categories.

使用有前景蒙版的类别级图像集合进行有限监督学习，推断出单个 RGB 图像的物体的 3D 形状、纹理和相机姿态，并展示其在大约 30 个物体类别上的应用可行性。

无标注图像集的隐式网格重建

Implicit Mesh Reconstruction from Unannotated Image Collections

Recently, learning-based algorithms for image inpainting achieve remarkable
progress dealing with squared or irregular holes. However, they fail to
generate plausible textures inside damaged area because there lacks surrounding
information. A progressive inpainting approach would be advantageous for
eliminating central blurriness, i.e., restoring well and then updating masks.
In this paper, we propose full-resolution residual network (FRRN) to fill
irregular holes, which is proved to be effective for progressive image
inpainting. We show that well-designed residual architecture facilitates
feature integration and texture prediction. Additionally, to guarantee
completion quality during progressive inpainting, we adopt N Blocks, One
Dilation strategy, which assigns several residual blocks for one dilation step.
Correspondingly, a step loss function is applied to improve the performance of
intermediate restorations. The experimental results demonstrate that the
proposed FRRN framework for image inpainting is much better than previous
methods both quantitatively and qualitatively. Our codes are released at:
https://github.com/ZongyuGuo/Inpainting_FRRN.

本文提出了一种基于 FRRN 的失真图像修复方法，采用了逐步恢复的方法，利用残差网络技术实现了特征融合和纹理预测，同时采用了 N Blocks 策略和步长损失函数，取得了在质量和数量上均优于以往文献的效果。

全分辨率残差网络渐进式图像修复

Progressive Image Inpainting with Full-Resolution Residual Network

We introduce a new silhouette-based representation for modeling clothed human
bodies using deep generative models. Our method can reconstruct a complete and
textured 3D model of a person wearing clothes from a single input picture.
Inspired by the visual hull algorithm, our implicit representation uses 2D
silhouettes and 3D joints of a body pose to describe the immense shape
complexity and variations of clothed people. Given a segmented 2D silhouette of
a person and its inferred 3D joints from the input picture, we first synthesize
consistent silhouettes from novel view points around the subject. The
synthesized silhouettes which are the most consistent with the input
segmentation are fed into a deep visual hull algorithm for robust 3D shape
prediction. We then infer the texture of the subject's back view using the
frontal image and segmentation mask as input to a conditional generative
adversarial network. Our experiments demonstrate that our silhouette-based
model is an effective representation and the appearance of the back view can be
predicted reliably using an image-to-image translation network. While classic
methods based on parametric models often fail for single-view images of
subjects with challenging clothing, our approach can still produce successful
results, which are comparable to those obtained from multi-view input.

本研究提出了一种基于轮廓的人体服装建模和纹理预测系统，使用深度生成模型重建穿着服装人体的完整 3D 模型，其中使用 2D 轮廓和 3D 关节来描述人体的形状复杂性和变化，再通过条件生成对抗网络预测背景面纹理，实验证明该模型是一种有效的表示方法。