We propose SplatArmor, a novel approach for recovering detailed and
animatable human models by `armoring' a parameterized body model with 3D
Gaussians. Our approach represents the human as a set of 3D Gaussians within a
canonical space, whose articulation is defined by extending the skinning of the
underlying SMPL geometry to arbitrary locations in the canonical space. To
account for pose-dependent effects, we introduce a SE(3) field, which allows us
to capture both the location and anisotropy of the Gaussians. Furthermore, we
propose the use of a neural color field to provide color regularization and 3D
supervision for the precise positioning of these Gaussians. We show that
Gaussian splatting provides an interesting alternative to neural rendering
based methods by leverging a rasterization primitive without facing any of the
non-differentiability and optimization challenges typically faced in such
approaches. The rasterization paradigms allows us to leverage forward skinning,
and does not suffer from the ambiguities associated with inverse skinning and
warping. We show compelling results on the ZJU MoCap and People Snapshot
datasets, which underscore the effectiveness of our method for controllable
human synthesis.

我们提出了一种名为 SplatArmor 的新方法，通过在 3D 高斯函数中将参数化的人体模型 “装甲”，从而恢复出详细的、可动画的人体模型。我们的方法将人体表示为一组位于规范空间内的 3D 高斯函数，其关节扭曲通过在规范空间内任意位置扩展底层 SMPL 几何体的蒙皮来定义。为了考虑姿势相关效果，我们引入了一个 SE (3) 场，允许捕捉高斯函数的位置和各向异性。此外，我们提出了使用神经颜色场来提供颜色规范化和用于准确定位这些高斯函数的 3D 监督。我们通过展示 ZJU MoCap 和 People Snapshot 数据集上的令人信服的结果，证明了高斯函数散射提供了一种有趣的替代于基于神经渲染的方法，它利用了不可区分性和优化挑战，而这些挑战通常在这类方法中面临。粗放复刻范例使我们能够利用向前蒙皮，并且不会受到逆蒙皮和扭曲所带来的不确定性的影响。我们的方法对于可控的人体合成的有效性进行了强有力的证明。

SplatArmor：来自单目 RGB 视频的可动人体的关节高斯样贴片渲染

SplatArmor: Articulated Gaussian splatting for animatable humans from  monocular RGB videos

In this report, we focus on reconstructing clothed humans in the canonical
space given multiple views and poses of a human as the input. To achieve this,
we utilize the geometric prior of the SMPLX model in the canonical space to
learn the implicit representation for geometry reconstruction. Based on the
observation that the topology between the posed mesh and the mesh in the
canonical space are consistent, we propose to learn latent codes on the posed
mesh by leveraging multiple input images and then assign the latent codes to
the mesh in the canonical space. Specifically, we first leverage normal and
geometry networks to extract the feature vector for each vertex on the SMPLX
mesh. Normal maps are adopted for better generalization to unseen images
compared to 2D images. Then, features for each vertex on the posed mesh from
multiple images are integrated by MLPs. The integrated features acting as the
latent code are anchored to the SMPLX mesh in the canonical space. Finally,
latent code for each 3D point is extracted and utilized to calculate the SDF.
Our work for reconstructing the human shape on canonical pose achieves 3rd
performance on WCPA MVP-Human Body Challenge.

本研究关注于使用多视图和多姿势的人体输入，重建位于规范空间中的衣着人体。我们利用 SMPLX 模型在规范空间中的几何先验，学习了几何重建的隐式表示，并在姿势网格和规范空间网格之间学习潜在编码。最终，我们的工作在 WCPA MVP-Human Body Challenge 中实现了第三的表现。

基于多视角和多姿态图像的人体模型的 Pixel2ISDF: 隐式符号距离场

Pixel2ISDF: Implicit Signed Distance Fields based Human Body Model from  Multi-view and Multi-pose Images

This paper tackles the task of category-level pose estimation for garments.
With a near infinite degree of freedom, a garment's full configuration (i.e.,
poses) is often described by the per-vertex 3D locations of its entire 3D
surface. However, garments are also commonly subject to extreme cases of
self-occlusion, especially when folded or crumpled, making it challenging to
perceive their full 3D surface. To address these challenges, we propose
GarmentNets, where the key idea is to formulate the deformable object pose
estimation problem as a shape completion task in the canonical space. This
canonical space is defined across garments instances within a category,
therefore, specifies the shared category-level pose. By mapping the observed
partial surface to the canonical space and completing it in this space, the
output representation describes the garment's full configuration using a
complete 3D mesh with the per-vertex canonical coordinate label. To properly
handle the thin 3D structure presented on garments, we proposed a novel 3D
shape representation using the generalized winding number field. Experiments
demonstrate that GarmentNets is able to generalize to unseen garment instances
and achieve significantly better performance compared to alternative
approaches.

本文提出了 GarmentNets，利用广义绕数场提出了一种新的 3D 形状表示，将形变物体姿态估计问题转化为标准空间中的形状完成任务，在标准空间中完成局部表面的映射并得到完整 3D 网格表示，使用该方法取得了比其他方法更好的性能。