We propose GGAvatar, a novel 3D avatar representation designed to robustly
model dynamic head avatars with complex identities and deformations. GGAvatar
employs a coarse-to-fine structure, featuring two core modules: Neutral
Gaussian Initialization Module and Geometry Morph Adjuster. Neutral Gaussian
Initialization Module pairs Gaussian primitives with deformable triangular
meshes, employing an adaptive density control strategy to model the geometric
structure of the target subject with neutral expressions. Geometry Morph
Adjuster introduces deformation bases for each Gaussian in global space,
creating fine-grained low-dimensional representations of deformation behaviors
to address the Linear Blend Skinning formula's limitations effectively.
Extensive experiments show that GGAvatar can produce high-fidelity renderings,
outperforming state-of-the-art methods in visual quality and quantitative
metrics.

GGAvatar 是一种新颖的 3D 头像表示方法，用于稳健地建模具有复杂身份和变形的动态头像。通过采用自适应密度控制策略将高斯原语与可变形三角网格配对，Neutral Gaussian Initialization Module 为目标主体的几何结构建模中性表情。Geometry Morph Adjuster 将全局空间中的适应变形基引入每个高斯，有效地创建形变行为的精细低维表示，以解决线性混合蒙皮公式的局限性。大量实验证明，GGAvatar 能够产生高保真度的渲染结果，在视觉质量和定量指标方面优于最先进的方法。

GGAvatar：高斯头部角度调整的几何化人物头像

GGAvatar: Geometric Adjustment of Gaussian Head Avatar

Visual localization, i.e., the problem of camera pose estimation, is a
central component of applications such as autonomous robots and augmented
reality systems. A dominant approach in the literature, shown to scale to large
scenes and to handle complex illumination and seasonal changes, is based on
local features extracted from images. The scene representation is a sparse
Structure-from-Motion point cloud that is tied to a specific local feature.
Switching to another feature type requires an expensive feature matching step
between the database images used to construct the point cloud. In this work, we
thus explore a more flexible alternative based on dense 3D meshes that does not
require features matching between database images to build the scene
representation. We show that this approach can achieve state-of-the-art
results. We further show that surprisingly competitive results can be obtained
when extracting features on renderings of these meshes, without any neural
rendering stage, and even when rendering raw scene geometry without color or
texture. Our results show that dense 3D model-based representations are a
promising alternative to existing representations and point to interesting and
challenging directions for future research.

通过探索一种基于密集三维网格的更为灵活的方法，本文旨在解决基于图像的特征匹配对在构建场景表示上造成的代价，并通过在渲染场景几何图形时提取特征的方式，展示了这一方法达到了最新成果。结果表明，基于密集三维模型的表示是现有表示的一种有前途的替代方案，并且为未来研究提出了有趣而具有挑战性的方向。

MeshLoc：基于网格的视觉定位

MeshLoc: Mesh-Based Visual Localization

We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and
novel view synthesis approach for general non-rigid dynamic scenes. Our
approach takes RGB images of a dynamic scene as input (e.g., from a monocular
video recording), and creates a high-quality space-time geometry and appearance
representation. We show that a single handheld consumer-grade camera is
sufficient to synthesize sophisticated renderings of a dynamic scene from novel
virtual camera views, e.g. a `bullet-time' video effect. NR-NeRF disentangles
the dynamic scene into a canonical volume and its deformation. Scene
deformation is implemented as ray bending, where straight rays are deformed
non-rigidly. We also propose a novel rigidity network to better constrain rigid
regions of the scene, leading to more stable results. The ray bending and
rigidity network are trained without explicit supervision. Our formulation
enables dense correspondence estimation across views and time, and compelling
video editing applications such as motion exaggeration. Our code will be open
sourced.

该论文提出了一种新的非刚性动态场景重建和新视角合成方法 ——NR-NeRF，该方法能够使用一个便携式消费级相机拍摄到的动态场景的 RGB 图像创建高质量的时空几何和外观表示，并且可以生成复杂的渲染场景。