VeGaS: Video Gaussian Splatting
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.
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Weronika Smolak-Dyżewska
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Dawid Malarz
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2024-11-19T16:26:14.135Z
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Dawid Malarz
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MalarzDawid
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Kornel Howil
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2024-11-19T13:09:06.082Z
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Kornel
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kornelhowil
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Jan Kaczmarczyk
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2024-11-20T10:08:59.307Z
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Jan Kaczmarczyk
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JasiekKaczmarczyk
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Marcin Mazur
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Przemysław Spurek
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2024-11-19T16:26:57.859Z
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Przemysław Spurek
user
przem85
VeGaS: Video Gaussian Splatting
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.
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.
Implicit Neural Representations INRs 3D Gaussian Splatting 3DGS Video Gaussian Representation VGR Folded-Gaussian distributions 2D Gaussians