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Research map / 3D vision / Face and shape

Learning a Neural 3D Texture Space From 2D Exemplars

CVPR 2020 · 3D vision › Face and shape · approach: Generative textures for 3D humans · 89 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use transformed multi-scale noise as the MLP input, rather than exposing absolute position (ablation)texture synthesis model · All texturessuccess: 8.23 -> 8.82Helped
Add learned transforms for the input noise fields (ablation)texture synthesis model · All texturessuccess: 8.54 -> 8.82Helped
Add learned transforms for the input noise fields (ablation)texture synthesis model · Woodsuccess: 5.40 -> 5.48Helped
Add learned transforms for the input noise fields (ablation)texture synthesis model · Grasssuccess: 21.7 -> 22.1Helped
Add learned transforms for the input noise fields (ablation)texture synthesis model · Marblesuccess: 9.3 -> 9.6Helped
Expose absolute position and omit transforms (ablation)texture synthesis model · All texturesdiversity: 93.4 -> 99.7Hurt
Replace pointwise neural sampling with a convolutional decodertexture synthesis model · 3D, 128 voxelstime: 1.50\,ms -> 551.59\,msHurt
Replace pointwise neural sampling with a convolutional decodertexture synthesis model · 3D, 128 voxelsmemory: 16\,M -> 646\,MHurt
Add an explicit diversity objective to the CNNcnn · All texturesdiversity: 0.5 -> 48.2Helped
Add explicit absolute position input (ablation)stochastic texture sampler · All texturesdiversityHurt

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