We present a novel framework for generating photorealistic 3D human head and
subsequently manipulating and reposing them with remarkable flexibility. The
proposed approach leverages an implicit function representation of 3D human
heads, employing 3D Gaussians anchored on a parametric face model. To enhance
representational capabilities and encode spatial information, we embed a
lightweight tri-plane payload within each Gaussian rather than directly storing
color and opacity. Additionally, we parameterize the Gaussians in a 2D UV space
via a 3DMM, enabling effective utilization of the diffusion model for 3D head
avatar generation. Our method facilitates the creation of diverse and realistic
3D human heads with fine-grained editing over facial features and expressions.
Extensive experiments demonstrate the effectiveness of our method.

我们提出了一个新颖的框架，用于生成逼真的 3D 人头，并以卓越的灵活性操纵和更改它们。

Gaussian3Diff：3D 高斯扩散用于 3D 全头部合成与编辑

Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and  Editing

In this paper, we propose StereoPIFu, which integrates the geometric
constraints of stereo vision with implicit function representation of PIFu, to
recover the 3D shape of the clothed human from a pair of low-cost rectified
images. First, we introduce the effective voxel-aligned features from a stereo
vision-based network to enable depth-aware reconstruction. Moreover, the novel
relative z-offset is employed to associate predicted high-fidelity human depth
and occupancy inference, which helps restore fine-level surface details.
Second, a network structure that fully utilizes the geometry information from
the stereo images is designed to improve the human body reconstruction quality.
Consequently, our StereoPIFu can naturally infer the human body's spatial
location in camera space and maintain the correct relative position of
different parts of the human body, which enables our method to capture human
performance. Compared with previous works, our StereoPIFu significantly
improves the robustness, completeness, and accuracy of the clothed human
reconstruction, which is demonstrated by extensive experimental results.

本文提出了 StereoPIFu 算法，它将立体视觉的几何约束与 PIFu 的隐式函数表示相结合，从一对低成本的矫正图像中恢复着装人体的 3D 形状，实验表明与先前的研究结果相比，StereoPIFu 显著提高了着装人体重建的稳健性、完整性和准确性。

StereoPIFu：基于双目视觉的深度感知衣着人体数字化

StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision

In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans),
a novel end-to-end framework for accurate reconstruction of animation-ready 3D
clothed humans from a monocular image. Existing approaches to digitize 3D
humans struggle to handle pose variations and recover details. Also, they do
not produce models that are animation ready. In contrast, ARCH is a learned
pose-aware model that produces detailed 3D rigged full-body human avatars from
a single unconstrained RGB image. A Semantic Space and a Semantic Deformation
Field are created using a parametric 3D body estimator. They allow the
transformation of 2D/3D clothed humans into a canonical space, reducing
ambiguities in geometry caused by pose variations and occlusions in training
data. Detailed surface geometry and appearance are learned using an implicit
function representation with spatial local features. Furthermore, we propose
additional per-pixel supervision on the 3D reconstruction using opacity-aware
differentiable rendering. Our experiments indicate that ARCH increases the
fidelity of the reconstructed humans. We obtain more than 50% lower
reconstruction errors for standard metrics compared to state-of-the-art methods
on public datasets. We also show numerous qualitative examples of animated,
high-quality reconstructed avatars unseen in the literature so far.

本文提出了 ARCH（可动画重建的带衣人），这是一种新型的端到端框架，用于从单个图像准确重建可动画的 3D 带衣人。与现有方法相比，ARCH 是一个基于学习的姿势感知模型，可以从单个自由 RGB 图像中生成详细的 3D 带骨骼全身人型，该模型通过创建语义空间和语义变形场来减少姿势变化和遮挡引起的几何模糊。通过空间局部特征的隐式函数表示来学习详细的几何形状和外观，同时提出了对 3D 重建的每个像素进行透明度感知的可微分渲染的额外像素监督。实验结果表明，ARCH 可以提高带衣人的重建保真度，并且与公共数据集上最先进的方法相比，重建误差降低了 50％以上。文中还展示了许多文献中未曾见过的高质量可动画重建人型的定性实例。