GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for
  Real-time Human Novel View Synthesis
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.
656e9b922e0a38afd19218ee
Shunyuan Zheng
656e9b922e0a38afd19218ef
Boyao Zhou
656e9b922e0a38afd19218f0
Ruizhi Shao
admin_assigned
2023-12-06T10:30:11.913Z
63f52856b51da4d61da7aa21
https://cdn-avatars.huggingface.co/v1/production/uploads/63f52856b51da4d61da7aa21/qSvSk1TnRp6DzdRzcoC8F.jpeg
Ruizhi Shao
user
Saurus
656e9b922e0a38afd19218f1
Boning Liu
656e9b922e0a38afd19218f2
Shengping Zhang
656e9b922e0a38afd19218f3
Liqiang Nie
656e9b922e0a38afd19218f4
Yebin Liu
admin_assigned
2023-12-05T12:20:19.679Z
62e14dbe4db2175cd2735a80
/avatars/e6385dcedcb97e1b36281b49210321aa.svg
Yebin Liu
user
YebinLiu
GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for
  Real-time Human Novel View Synthesis
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.
A GPS-Gaussian approach synthesizes 2K-resolution views in real-time using Gaussian parameter maps and depth estimation, outperforming existing methods.
Gaussian parameter maps Gaussian Splatting neural implicit rendering depth estimation module 2D parameter maps 3D space differentiable framework novel view synthesis