GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for
  Real-time Human Novel View Synthesis
Authors: name: Shunyuan Zheng, name: Boyao Zhou, name: Ruizhi Shao, name: Boning Liu, name: Shengping Zhang, name: Liqiang Nie, name: Yebin Liu

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.