SVGS improves Gaussian Splatting novel-view synthesis by replacing single-color primitives with spatially varying color and opacity functions implemented via bilinear interpolation, movable kernels, or tiny neural networks on 2D Gaussian surfels.
Structure- from-motion revisited
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HOGS renders physically plausible human-object interactions from sparse views by optimizing dynamic 3D Gaussians with contact/separation losses guided by pre-trained pose refiner and contact predictor modules, claiming SOTA quality and efficiency.
citing papers explorer
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SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors
SVGS improves Gaussian Splatting novel-view synthesis by replacing single-color primitives with spatially varying color and opacity functions implemented via bilinear interpolation, movable kernels, or tiny neural networks on 2D Gaussian surfels.
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Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting
HOGS renders physically plausible human-object interactions from sparse views by optimizing dynamic 3D Gaussians with contact/separation losses guided by pre-trained pose refiner and contact predictor modules, claiming SOTA quality and efficiency.