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Human Shape From Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks

CVPR 2017 · 3D vision › Human pose estimation · approach: Human shape and pose regression · 112 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the proposed SFUS-HKS-1 method instead of HS-Net-1-SHuman shape estimation from a scaled frontal silhouette · Synthetic body measurements, one scaled frontal silhouettemean error (mm): 10.8 mm -> 4.01 mmHelped
Use the proposed UF-US-HKS-2 method instead of HS-2-Net-MMHuman shape estimation from two views · Synthetic body measurements, two viewsmean error (mm): 10.8 mm -> 3.77 mmHelped
Add a side-view silhouette during training (ablation)CMNN single-view prediction · Synthetic body measurements, one scaled frontal silhouettemean errorHelped
Add an unscaled frontal view during training (ablation)CMNN single-view prediction · Synthetic body measurements, one scaled frontal silhouettemean errorHelped
Train with scaled frontal, unscaled frontal, and scaled side views (ablation)CMNN single-view prediction · Synthetic body measurements, one scaled frontal silhouettemean errorHelped
Jointly train HKS-Net with CMNN rather than training HKS-Net separately (ablation)HKS-Net shape embedding · HKS-Net mesh reconstructionmean error (mm): 4.74 mm -> 3.77 mmHelped
Train and test with different poses (ablation)SF-1-P · Synthetic body measurements, one scaled frontal silhouettemean errorNo clear effect
Add high levels of noise to silhouettesProposed method · Silhouette noise testmean error increase (mm): +4.1 mmHurt

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