We present a new approach, termed GPS-Gaussian, for synthesizing novel views
of a character in a real-time manner. The proposed method enables 2K-resolution
rendering under a sparse-view camera setting. Unlike the original Gaussian
Splatting or neural implicit rendering methods that necessitate per-subject
optimizations, we introduce Gaussian parameter maps defined on the source views
and regress directly Gaussian Splatting properties for instant novel view
synthesis without any fine-tuning or optimization. To this end, we train our
Gaussian parameter regression module on a large amount of human scan data,
jointly with a depth estimation module to lift 2D parameter maps to 3D space.
The proposed framework is fully differentiable and experiments on several
datasets demonstrate that our method outperforms state-of-the-art methods while
achieving an exceeding rendering speed.

通过 GPS-Gaussian 方法，我们可以实时合成一个字符的新视图，以稀疏的视图相机设置进行 2K 分辨率渲染。通过在源视图上定义高斯参数图并直接回归高斯喷洒属性，我们无需进行任何微调或优化即可实现即时的新视图合成。我们通过大量的人体扫描数据对高斯参数回归模块进行训练，同时使用深度估计模块将 2D 参数图转化为 3D 空间。实验结果表明，我们的方法在几个数据集上优于最先进的方法，同时实现了非凡的渲染速度。

GPS 高斯：用于实时人体新视角合成的通用像素级三维高斯扩散

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for  Real-time Human Novel View Synthesis

Very recently neural implicit rendering techniques have been rapidly evolved
and shown great advantages in novel view synthesis and 3D scene reconstruction.
However, existing neural rendering methods for editing purposes offer limited
functionality, e.g., rigid transformation, or not applicable for fine-grained
editing for general objects from daily lives. In this paper, we present a novel
mesh-based representation by encoding the neural implicit field with
disentangled geometry and texture codes on mesh vertices, which facilitates a
set of editing functionalities, including mesh-guided geometry editing,
designated texture editing with texture swapping, filling and painting
operations. To this end, we develop several techniques including learnable sign
indicators to magnify spatial distinguishability of mesh-based representation,
distillation and fine-tuning mechanism to make a steady convergence, and the
spatial-aware optimization strategy to realize precise texture editing.
Extensive experiments and editing examples on both real and synthetic data
demonstrate the superiority of our method on representation quality and editing
ability. Code is available on the project webpage:
this https URL

本论文提出了一种基于网格的新型表示法，并将神经隐式场编码为网格顶点上的几何和纹理码，以实现包括网格引导几何编辑、指定纹理编辑、填充和绘画操作等一系列编辑功能。实验结果表明，该方法有着优秀的表现质量和编辑能力。