Recently, implicit neural representation has been widely used to generate
animatable human avatars. However, the materials and geometry of those
representations are coupled in the neural network and hard to edit, which
hinders their application in traditional graphics engines. We present a
framework for acquiring human avatars that are attached with high-resolution
physically-based material textures and triangular mesh from monocular video.
Our method introduces a novel information fusion strategy to combine the
information from the monocular video and synthesize virtual multi-view images
to tackle the sparsity of the input view. We reconstruct humans as deformable
neural implicit surfaces and extract triangle mesh in a well-behaved pose as
the initial mesh of the next stage. In addition, we introduce an approach to
correct the bias for the boundary and size of the coarse mesh extracted.
Finally, we adapt prior knowledge of the latent diffusion model at
super-resolution in multi-view to distill the decomposed texture. Experiments
show that our approach outperforms previous representations in terms of high
fidelity, and this explicit result supports deployment on common renderers.

通过单目视频实现高分辨率物理材质纹理和三角网格的人体模型的获取并结合多视角图像合成的信息融合策略，通过神经隐式表达生成可编辑的变形人体模型。

HR 人形：利用三角网格和视频高分辨率纹理建模人形化身

HR Human: Modeling Human Avatars with Triangular Mesh and  High-Resolution Textures from Videos

Recent advances in radiance fields enable photorealistic rendering of static
or dynamic 3D scenes, but still do not support explicit deformation that is
used for scene manipulation or animation. In this paper, we propose a method
that enables a new type of deformation of the radiance field: free-form
radiance field deformation. We use a triangular mesh that encloses the
foreground object called cage as an interface, and by manipulating the cage
vertices, our approach enables the free-form deformation of the radiance field.
The core of our approach is cage-based deformation which is commonly used in
mesh deformation. We propose a novel formulation to extend it to the radiance
field, which maps the position and the view direction of the sampling points
from the deformed space to the canonical space, thus enabling the rendering of
the deformed scene. The deformation results of the synthetic datasets and the
real-world datasets demonstrate the effectiveness of our approach.

本文提出了一种基于三角网格和笼状变形的方法，实现了光度场的自由形变，从而实现了可操纵和渲染的 3D 场景。

用笼子变形辐射场

Deforming Radiance Fields with Cages

Most attempts to represent 3D shapes for deep learning have focused on
volumetric grids, multi-view images and point clouds. In this paper we look at
the most popular representation of 3D shapes in computer graphics - a
triangular mesh - and ask how it can be utilized within deep learning. The few
attempts to answer this question propose to adapt convolutions & pooling to
suit Convolutional Neural Networks (CNNs). This paper proposes a very different
approach, termed MeshWalker, to learn the shape directly from a given mesh. The
key idea is to represent the mesh by random walks along the surface, which
"explore" the mesh's geometry and topology. Each walk is organized as a list of
vertices, which in some manner imposes regularity on the mesh. The walk is fed
into a Recurrent Neural Network (RNN) that "remembers" the history of the walk.
We show that our approach achieves state-of-the-art results for two fundamental
shape analysis tasks: shape classification and semantic segmentation.
Furthermore, even a very small number of examples suffices for learning. This
is highly important, since large datasets of meshes are difficult to acquire.

本文提出了一种新方法 MeshWalker，利用随机游走算法，在三角网格结构的 3D 形状中探索和学习几何和拓扑特征。该方法在形状分类和语义分割方面表现良好，并且仅需要很少量的样本即可进行学习。

MeshWalker: 随机游走实现深度网格理解

MeshWalker: Deep Mesh Understanding by Random Walks

When fitting Gaussian Mixture Models to 3D geometry, the model is typically
fit to point clouds, even when the shapes were obtained as 3D meshes. Here we
present a formulation for fitting Gaussian Mixture Models (GMMs) directly to a
triangular mesh instead of using points sampled from its surface. Part of this
work analyzes a general formulation for evaluating likelihood of geometric
objects. This modification enables fitting higher-quality GMMs under a wider
range of initialization conditions. Additionally, models obtained from this
fitting method are shown to produce an improvement in 3D registration for both
meshes and RGB-D frames. This result is general and applicable to arbitrary
geometric objects, including representing uncertainty from sensor measurements.

该论文提出了一种将高斯混合模型直接拟合于三角网格的新方法，这可提高几何目标的似然性评估，并产生更高质量的 GMMs 以及对于网格和 RGB-D 框架的 3D 配准，该方法具有普适性。