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Distilling Neural Fields for Real-Time Articulated Shape Reconstruction

CVPR 2023 · 3D vision › Face and shape · approach: Articulated and non-rigid shape learning · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
distill dynamic NeRF supervision into a feed-forward predictorDASR · AMA human sequencesinference time per frame: 43000 -> 22Helped
add a temporal encoder (ablation)DASR · AMA T_sambaCD (cm): 12.2 -> 10.6Helped
use a Conv1D temporal encoder instead of the full method's temporal encoder (ablation)DASR · AMA sequencesCD (cm): 10.6 -> 10.8; 17.3 -> 15.0Mixed
use a Transformer temporal encoder instead of the full method's temporal encoder (ablation)DASR · AMA sequencesCD (cm): 10.6 -> 10.6; 12.0 -> 13.2Mixed
remove frozen teacher decoders (ablation)DASR · AMA sequencesCD (cm): 10.6 -> 11.0; 12.0 -> 11.5Mixed
extract the template using a 64³ grid instead of a 128³ grid (ablation)DASR · AMA T_sambaF@2%: 49.6 -> 42.2Hurt
extract the template using a 256³ grid instead of a 128³ grid (ablation)DASR · AMA T_sambaF@2%: 49.6 -> 50.7No clear effect
train with 5 videos instead of 35 (ablation)DASR · AMA T_sambaCD (cm): 10.8 -> 11.2Hurt
train with 16 videos instead of 35 (ablation)DASR · AMA T_sambaCD (cm): 10.8 -> 10.5Helped
train with 26 videos instead of 35 (ablation)DASR · AMA T_sambaCD (cm): 10.8 -> 10.2Helped
train with 35 videos rather than 26, adding unrelated identities (ablation)DASR · AMA T_sambaCD (cm): 10.2 -> 10.8Hurt

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