A self-supervised method pretrains an encoder on eight PSP images per view to learn generalizable subsurface scattering representations that transfer to relighting and dense footprint reconstruction on unseen complex objects.
V ox-e: Editing 3d scenes by talking to gen- erative models
3 Pith papers cite this work. Polarity classification is still indexing.
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Voxify3D generates voxel art from 3D meshes via orthographic pixel supervision, patch-based CLIP alignment, and palette-constrained Gumbel-Softmax quantization, achieving 37.12 CLIP-IQA and 77.90% user preference.
U-Net learns dense pixel footprint responses for subsurface scattering from phase-shifted profilometry data, supporting relighting and generalization across materials.
citing papers explorer
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From Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs
A self-supervised method pretrains an encoder on eight PSP images per view to learn generalizable subsurface scattering representations that transfer to relighting and dense footprint reconstruction on unseen complex objects.
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Voxify3D: Pixel Art Meets Volumetric Rendering
Voxify3D generates voxel art from 3D meshes via orthographic pixel supervision, patch-based CLIP alignment, and palette-constrained Gumbel-Softmax quantization, achieving 37.12 CLIP-IQA and 77.90% user preference.
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Neural Acquisition & Representation of Subsurface Scattering
U-Net learns dense pixel footprint responses for subsurface scattering from phase-shifted profilometry data, supporting relighting and generalization across materials.