3D Gaussian splatting, emerging as a groundbreaking approach, has drawn
increasing attention for its capabilities of high-fidelity reconstruction and
real-time rendering. However, it couples the appearance and geometry of the
scene within the Gaussian attributes, which hinders the flexibility of editing
operations, such as texture swapping. To address this issue, we propose a novel
approach, namely Texture-GS, to disentangle the appearance from the geometry by
representing it as a 2D texture mapped onto the 3D surface, thereby
facilitating appearance editing. Technically, the disentanglement is achieved
by our proposed texture mapping module, which consists of a UV mapping MLP to
learn the UV coordinates for the 3D Gaussian centers, a local Taylor expansion
of the MLP to efficiently approximate the UV coordinates for the ray-Gaussian
intersections, and a learnable texture to capture the fine-grained appearance.
Extensive experiments on the DTU dataset demonstrate that our method not only
facilitates high-fidelity appearance editing but also achieves real-time
rendering on consumer-level devices, e.g. a single RTX 2080 Ti GPU.

通过提出一种称为 Texture-GS 的新方法，使用 3D 高斯喷射技术，可以实现高保真的外观编辑和实时渲染。它通过将外观和几何表示为附着在三维表面上的二维纹理来实现外观与几何的解离，从而增强了编辑操作的灵活性。通过在 DTU 数据集上进行广泛实验证明，该方法不仅实现了高保真度的外观编辑，还在消费级设备上实现了实时渲染。

Texture-GS: 三维高斯点描编辑中几何与纹理的解藕

Texture-GS: Disentangling the Geometry and Texture for 3D Gaussian  Splatting Editing

Limited by the nature of the low-dimensional representational capacity of
3DMM, most of the 3DMM-based face reconstruction (FR) methods fail to recover
high-frequency facial details, such as wrinkles, dimples, etc. Some attempt to
solve the problem by introducing detail maps or non-linear operations, however,
the results are still not vivid. To this end, we in this paper present a novel
hierarchical representation network (HRN) to achieve accurate and detailed face
reconstruction from a single image. Specifically, we implement the geometry
disentanglement and introduce the hierarchical representation to fulfill
detailed face modeling. Meanwhile, 3D priors of facial details are incorporated
to enhance the accuracy and authenticity of the reconstruction results. We also
propose a de-retouching module to achieve better decoupling of the geometry and
appearance. It is noteworthy that our framework can be extended to a multi-view
fashion by considering detail consistency of different views. Extensive
experiments on two single-view and two multi-view FR benchmarks demonstrate
that our method outperforms the existing methods in both reconstruction
accuracy and visual effects. Finally, we introduce a high-quality 3D face
dataset FaceHD-100 to boost the research of high-fidelity face reconstruction.
The project homepage is at this https URL

本文介绍了一种新的分层表示网络（HRN），通过几何分离和分层表示来实现对细节精确的面部重建。同时，考虑不同视角的细节一致性，提出了一种去除重新贴图模块。通过在两个单视图和两个多视图 FR 基准上的实验，展示了该方法在重建精度和视觉效果方面的优越性，并引入了高质量的 3D 面部数据集 FaceHD-100。