Fueled by the power of deep learning techniques and implicit shape learning,
recent advances in single-image human digitalization have reached unprecedented
accuracy and could recover fine-grained surface details such as garment
wrinkles. However, a common problem for the implicit-based methods is that they
cannot produce separated and topology-consistent mesh for each garment piece,
which is crucial for the current 3D content creation pipeline. To address this
issue, we proposed a novel geometry inference framework ReEF that reconstructs
topology-consistent layered garment mesh by registering the explicit garment
template to the whole-body implicit fields predicted from single images.
Experiments demonstrate that our method notably outperforms its counterparts on
single-image layered garment reconstruction and could bring high-quality
digital assets for further content creation.

利用深度学习技术和隐含形状学习的力量，提出一种新颖的几何推断框架 ReEF，通过将显式模板与单张图片生成的整体隐式场进行匹配，重建具有拓扑一致性的分层服装网格，实验表明我们的方法在单张图像重建方面优于同类方法并能够为进一步的内容创作提供高质量的数字资源

从单张图片恢复高保真服装网格模型：从显式到隐式

Registering Explicit to Implicit: Towards High-Fidelity Garment mesh  Reconstruction from Single Images

High-fidelity face digitization solutions often combine multi-view stereo
(MVS) techniques for 3D reconstruction and a non-rigid registration step to
establish dense correspondence across identities and expressions. A common
problem is the need for manual clean-up after the MVS step, as 3D scans are
typically affected by noise and outliers and contain hairy surface regions that
need to be cleaned up by artists. Furthermore, mesh registration tends to fail
for extreme facial expressions. Most learning-based methods use an underlying
3D morphable model (3DMM) to ensure robustness, but this limits the output
accuracy for extreme facial expressions. In addition, the global bottleneck of
regression architectures cannot produce meshes that tightly fit the ground
truth surfaces. We propose ToFu, Topologically consistent Face from multi-view,
a geometry inference framework that can produce topologically consistent meshes
across facial identities and expressions using a volumetric representation
instead of an explicit underlying 3DMM. Our novel progressive mesh generation
network embeds the topological structure of the face in a feature volume,
sampled from geometry-aware local features. A coarse-to-fine architecture
facilitates dense and accurate facial mesh predictions in a consistent mesh
topology. ToFu further captures displacement maps for pore-level geometric
details and facilitates high-quality rendering in the form of albedo and
specular reflectance maps. These high-quality assets are readily usable by
production studios for avatar creation, animation and physically-based skin
rendering. We demonstrate state-of-the-art geometric and correspondence
accuracy, while only taking 0.385 seconds to compute a mesh with 10K vertices,
which is three orders of magnitude faster than traditional techniques. The code
and the model are available for research purposes at
this https URL

该论文提出了一种名为 ToFu 的几何推理框架，它使用一种体积表示法来生成面部网格，可以跨面部身份和表情产生具有拓扑一致性的网格。该方法通过考虑面部几何学特征，采用从粗到细的神经网络结构来实现稠密准确的面部网格预测，并在 pore-level 几何细节方面捕捉位移图，以生成高质量的渲染效果。

使用体积采样进行拓扑一致的多视角人脸推断

Topologically Consistent Multi-View Face Inference Using Volumetric  Sampling

We propose a method for room layout estimation that does not rely on the
typical box approximation or Manhattan world assumption. Instead, we
reformulate the geometry inference problem as an instance detection task, which
we solve by directly regressing 3D planes using an R-CNN. We then use a variant
of probabilistic clustering to combine the 3D planes regressed at each frame in
a video sequence, with their respective camera poses, into a single global 3D
room layout estimate. Finally, we showcase results which make no assumptions
about perpendicular alignment, so can deal effectively with walls in any
alignment.

我们提出了一个不依赖于典型框近似或曼哈顿世界假设的房间布局估计方法，将几何推断问题重新定义为实例检测任务，通过使用 R-CNN 直接回归 3D 平面来解决该问题，然后使用一种概率聚类的变体来结合每个视频序列中的每帧回归的 3D 平面及其相应的相机姿态，并得到一个单个的全局 3D 房间布局估计结果；最后，我们展示了在不假设垂直对准的情况下处理任何对齐的墙壁的结果。