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TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style

CVPR 2020 · 3D vision › Face and shape · approach: Clothed human modeling · 307 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the high/low-frequency decomposition and narrow-bandwidth mixture instead of the joint MLP baselineTailorNet · TailorNet split 2 (train styles, test poses)mean per-vertex error (mm): 10.6 -> 10.2Helped
Use the high/low-frequency decomposition and narrow-bandwidth mixture instead of the joint MLP baselineTailorNet · TailorNet split 3 (test styles, train poses)mean per-vertex error (mm): 11.7 -> 11.4Helped
Use the high/low-frequency decomposition and narrow-bandwidth mixture instead of the joint MLP baselineTailorNet · TailorNet split 4 (test styles, test poses)mean per-vertex error (mm): 11.6 -> 11.4Helped
Use an MLP instead of a UV Decoder for pose-dependent deformationSingle style-shape model · Loose-fit style-shape pairmean per-vertex error (mm): 15.9 -> 14.5Helped
Use an MLP instead of a Graph CNN for pose-dependent deformationSingle style-shape model · Loose-fit style-shape pairmean per-vertex error (mm): 16.1 -> 14.5Helped
Use an MLP instead of a UV Decoder for pose-dependent deformationSingle style-shape model · Tight-fit style-shape pairmean per-vertex error (mm): 11.4 -> 10.1Helped
Use an MLP instead of a Graph CNN for pose-dependent deformationSingle style-shape model · Tight-fit style-shape pairmean per-vertex error (mm): 11.7 -> 10.1Helped
Remove the frequency decomposition and predict style-shape prototype displacements directly (ablation)TailorNet mixture model · Clothing wrinkle-detail predictionwrinkle detail retentionHurt
Combine training examples across styles and shapes in a joint MLPTailorNet joint baseline · Clothing deformation across styles and shapesvisual realismHurt

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