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。