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