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

A long-standing goal in scene understanding is to obtain interpretable and
editable representations that can be directly constructed from a raw monocular
RGB-D video, without requiring specialized hardware setup or priors. The
problem is significantly more challenging in the presence of multiple moving
and/or deforming objects. Traditional methods have approached the setup with a
mix of simplifications, scene priors, pretrained templates, or known
deformation models. The advent of neural representations, especially neural
implicit representations and radiance fields, opens the possibility of
end-to-end optimization to collectively capture geometry, appearance, and
object motion. However, current approaches produce global scene encoding,
assume multiview capture with limited or no motion in the scenes, and do not
facilitate easy manipulation beyond novel view synthesis. In this work, we
introduce a factored neural scene representation that can directly be learned
from a monocular RGB-D video to produce object-level neural presentations with
an explicit encoding of object movement (e.g., rigid trajectory) and/or
deformations (e.g., nonrigid movement). We evaluate ours against a set of
neural approaches on both synthetic and real data to demonstrate that the
representation is efficient, interpretable, and editable (e.g., change object
trajectory). The project webpage is available at:
$\href{this https URL}{\text{link}}$.

本研究提出了一种基于神经网络的场景表示方法，可以直接从 RGB-D 视频中学习物体级别的神经表示，并具有显式的对象运动编码和 / 或变形编码，该方法评估后表明具有高效性，可解释性和可编辑性。

场景理解的分解神经表示

Factored Neural Representation for Scene Understanding

How do we learn an object detector that is invariant to occlusions and
deformations? Our current solution is to use a data-driven strategy -- collect
large-scale datasets which have object instances under different conditions.
The hope is that the final classifier can use these examples to learn
invariances. But is it really possible to see all the occlusions in a dataset?
We argue that like categories, occlusions and object deformations also follow a
long-tail. Some occlusions and deformations are so rare that they hardly
happen; yet we want to learn a model invariant to such occurrences. In this
paper, we propose an alternative solution. We propose to learn an adversarial
network that generates examples with occlusions and deformations. The goal of
the adversary is to generate examples that are difficult for the object
detector to classify. In our framework both the original detector and adversary
are learned in a joint manner. Our experimental results indicate a 2.3% mAP
boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge
compared to the Fast-RCNN pipeline. We also release the code for this paper.

本文提出了使用对抗网络生成带有遮挡和变形的示例来学习不变性的方法，该方法在 VOC07 和 VOC2012 目标检测挑战中取得了 2.3％和 2.6％的 mAP 提升。