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Research map / 3D vision / NeRF and radiance fields

HumanNeRF: Efficiently Generated Human Radiance Field From Sparse Inputs

CVPR 2022 · 3D vision › NeRF and radiance fields · approach: Inverse rendering with neural fields · 117 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use HumanNeRF instead of IBRNetnovel-view synthesis on real testing data · Real testing dataPSNR: 30.73 -> 33.01Helped
Use HumanNeRF instead of IBRNetnovel-view synthesis on real testing data · Real testing dataSSIM: 0.9817 -> 0.9842Helped
Use HumanNeRF instead of IBRNetnovel-view synthesis on real testing data · Real testing dataLPIPS: 0.0348 -> 0.0334Helped
Use HumanNeRF instead of IBRNetnovel-view synthesis on real testing data · Real testing dataMAE: 1.154 -> 0.9307Helped
Add aggregated pixel-alignment features (ablation)HumanNeRF · Real testing dataPSNR: 18.36 -> 33.01Helped
Add pose-embedded non-rigid deformation (ablation)HumanNeRF · Real testing dataPSNR: 26.79 -> 33.01Helped
Add neural appearance blending (ablation)HumanNeRF · Real testing dataPSNR: 29.69 -> 33.01Helped
Add per-subject fine-tuning (ablation)HumanNeRF · Real testing dataPSNR: 29.51 -> 33.01Helped
Add appearance blending (ablation)HumanNeRF without blending · Real testing dataPSNR: 25.80 -> 33.01Helped
Add radiance-field rendering (ablation)HumanNeRF without radiance field · Real testing dataPSNR: 29.69 -> 33.01Helped
Increase input views from two to four (ablation)HumanNeRF · Real testing dataPSNR: 22.44 -> 25.88Helped
Increase input views from four to six (ablation)HumanNeRF · Real testing dataPSNR: 25.88 -> 32.59Helped

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