title: VeGaS: Video Gaussian Splatting
summary: Implicit Neural Representations (INRs) employ neural networks to approximate
  discrete data as continuous functions. In the context of video data, such
  models can be utilized to transform the coordinates of pixel locations along
  with frame occurrence times (or indices) into RGB color values. Although INRs
  facilitate effective compression, they are unsuitable for editing purposes. One
  potential solution is to use a 3D Gaussian Splatting (3DGS) based model, such
  as the Video Gaussian Representation (VGR), which is capable of encoding video
  as a multitude of 3D Gaussians and is applicable for numerous video processing
  operations, including editing. Nevertheless, in this case, the capacity for
  modification is constrained to a limited set of basic transformations. To
  address this issue, we introduce the Video Gaussian Splatting (VeGaS) model,
  which enables realistic modifications of video data. To construct VeGaS, we
  propose a novel family of Folded-Gaussian distributions designed to capture
  nonlinear dynamics in a video stream and model consecutive frames by 2D
  Gaussians obtained as respective conditional distributions. Our experiments
  demonstrate that VeGaS outperforms state-of-the-art solutions in frame
  reconstruction tasks and allows realistic modifications of video data. The code
  is available at: https://github.com/gmum/VeGaS.
paper_authors._id: 673c8d3a44ea7d8aeaa3ef3e
paper_authors.name: Weronika Smolak-Dyżewska
paper_authors._id: 673c8d3a44ea7d8aeaa3ef3f
paper_authors.name: Dawid Malarz
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paper_authors.fullname: Dawid Malarz
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paper_authors.user: MalarzDawid
paper_authors._id: 673c8d3a44ea7d8aeaa3ef40
paper_authors.name: Kornel Howil
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paper_authors.fullname: Kornel
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paper_authors.user: kornelhowil
paper_authors._id: 673c8d3a44ea7d8aeaa3ef41
paper_authors.name: Jan Kaczmarczyk
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paper_authors.fullname: Jan Kaczmarczyk
paper_authors.type: user
paper_authors.user: JasiekKaczmarczyk
paper_authors._id: 673c8d3a44ea7d8aeaa3ef42
paper_authors.name: Marcin Mazur
paper_authors._id: 673c8d3a44ea7d8aeaa3ef43
paper_authors.name: Przemysław Spurek
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T16:26:57.859Z
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paper_authors.fullname: Przemysław Spurek
paper_authors.type: user
paper_authors.user: przem85
paper_title: VeGaS: Video Gaussian Splatting
paper_summary: Implicit Neural Representations (INRs) employ neural networks to approximate
  discrete data as continuous functions. In the context of video data, such
  models can be utilized to transform the coordinates of pixel locations along
  with frame occurrence times (or indices) into RGB color values. Although INRs
  facilitate effective compression, they are unsuitable for editing purposes. One
  potential solution is to use a 3D Gaussian Splatting (3DGS) based model, such
  as the Video Gaussian Representation (VGR), which is capable of encoding video
  as a multitude of 3D Gaussians and is applicable for numerous video processing
  operations, including editing. Nevertheless, in this case, the capacity for
  modification is constrained to a limited set of basic transformations. To
  address this issue, we introduce the Video Gaussian Splatting (VeGaS) model,
  which enables realistic modifications of video data. To construct VeGaS, we
  propose a novel family of Folded-Gaussian distributions designed to capture
  nonlinear dynamics in a video stream and model consecutive frames by 2D
  Gaussians obtained as respective conditional distributions. Our experiments
  demonstrate that VeGaS outperforms state-of-the-art solutions in frame
  reconstruction tasks and allows realistic modifications of video data. The code
  is available at: https://github.com/gmum/VeGaS.
paper_ai_summary: The Video Gaussian Splatting (VeGaS) model uses a novel Folded-Gaussian distribution to enable realistic modifications of video data and performs well in frame reconstruction tasks.
paper_ai_keywords: Implicit Neural Representations INRs 3D Gaussian Splatting 3DGS Video Gaussian Representation VGR Folded-Gaussian distributions 2D Gaussians