SV-GS estimates a time-dependent skeleton pose plus fine deformations to enable 4D Gaussian splatting from sparse views, outperforming prior sparse methods by up to 34% PSNR on synthetic data and matching dense monocular baselines on real data with far fewer frames.
Watch it move: Unsupervised discovery of 3d joints for re-posing of articulated objects
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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GenMatter is a generative hierarchical model that groups low-level motion and high-level features into particles and clusters representing independently moveable physical entities, validated across dot kinematograms, camouflaged objects, and RGB videos.
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SV-GS: Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting
SV-GS estimates a time-dependent skeleton pose plus fine deformations to enable 4D Gaussian splatting from sparse views, outperforming prior sparse methods by up to 34% PSNR on synthetic data and matching dense monocular baselines on real data with far fewer frames.
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GenMatter: Perceiving Physical Objects with Generative Matter Models
GenMatter is a generative hierarchical model that groups low-level motion and high-level features into particles and clusters representing independently moveable physical entities, validated across dot kinematograms, camouflaged objects, and RGB videos.