AI RESEARCH
Generative 3D Gaussians with Learned Density Control
arXiv CS.CV
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ArXi:2605.16355v1 Announce Type: cross We present Density-Sampled Gaussians (DeG), a novel 3D representation designed to bridge the gap between adaptive rendering primitives and scalable generative modeling. Unlike existing approaches that constrain 3D Gaussians to fixed voxel grids or arrays, DeG models Gaussian centers as samples from a learnable probability density function defined over an octree.