We present FITE, a First-Implicit-Then-Explicit framework for modeling human
avatars in clothing. Our framework first learns implicit surface templates
representing the coarse clothing topology, and then employs the templates to
guide the generation of point sets which further capture pose-dependent
clothing deformations such as wrinkles. Our pipeline incorporates the merits of
both implicit and explicit representations, namely, the ability to handle
varying topology and the ability to efficiently capture fine details. We also
propose diffused skinning to facilitate template training especially for loose
clothing, and projection-based pose-encoding to extract pose information from
mesh templates without predefined UV map or connectivity. Our code is publicly
available at this https URL

该论文介绍了 FITE 框架，通过学习隐式表面模板来表示粗糙的衣服拓扑，然后使用模板指导点集生成以捕捉皱褶等姿态依赖性衣服变形。该框架结合了隐式和显式表示的优点，能够处理不同的拓扑结构和有效地捕捉细节。此外，还提出了扩散蒙皮技术来进行模板训练以处理宽松的衣物，并提出基于投影的姿态编码方法，从网格模板中提取姿态信息。

点云衣着人体建模的学习隐式模板

Learning Implicit Templates for Point-Based Clothed Human Modeling

Learning to model and reconstruct humans in clothing is challenging due to
articulation, non-rigid deformation, and varying clothing types and topologies.
To enable learning, the choice of representation is the key. Recent work uses
neural networks to parameterize local surface elements. This approach captures
locally coherent geometry and non-planar details, can deal with varying
topology, and does not require registered training data. However, naively using
such methods to model 3D clothed humans fails to capture fine-grained local
deformations and generalizes poorly. To address this, we present three key
innovations: First, we deform surface elements based on a human body model such
that large-scale deformations caused by articulation are explicitly separated
from topological changes and local clothing deformations. Second, we address
the limitations of existing neural surface elements by regressing local
geometry from local features, significantly improving the expressiveness.
Third, we learn a pose embedding on a 2D parameterization space that encodes
posed body geometry, improving generalization to unseen poses by reducing
non-local spurious correlations. We demonstrate the efficacy of our surface
representation by learning models of complex clothing from point clouds. The
clothing can change topology and deviate from the topology of the body. Once
learned, we can animate previously unseen motions, producing high-quality point
clouds, from which we generate realistic images with neural rendering. We
assess the importance of each technical contribution and show that our approach
outperforms the state-of-the-art methods in terms of reconstruction accuracy
and inference time. The code is available for research purposes at
this https URL .

通过使用神经网络的表征来捕捉局部相干几何信息和非平面细节，并回归本地几何，我们提出了一种新的表面能量，通过在服装上基于人体模型的变形将人体和衣服的形变分离开来，在二维参数空间上学习一种姿态嵌入，对未知的姿态进行模型建立，以及通过动作瞬间学习人的姿态，实现了点云三维建模和渲染。