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.
Gaussian Splatting neural parameter maps novel view synthesis sparse-view camera parameter regression Gaussian parameter maps depth estimation rendering speed