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Research map / 3D vision / Face and shape

BANMo: Building Animatable 3D Neural Models From Many Casual Videos

CVPR 2022 · 3D vision › Face and shape · approach: Articulated and non-rigid shape learning · 125 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use BANMo instead of ViSER3D reconstruction · AMA-swingChamfer distance (cm): 15.7 -> 9.1Helped
Use BANMo instead of ViSER3D reconstruction · EagleF@2% (%): 20.6 -> 56.7Helped
Use BANMo instead of ViSER3D reconstruction · HandsChamfer distance (cm): 16.8 -> 7.5Helped
Add canonical embeddings and their registration losses (ablation)BANMo · Multiple videosregistration qualityHelped
Add measured optical-flow reconstruction loss (ablation)BANMo · Multiple videosregistration qualityHelped
Use neural blend skinning instead of MLP-SE(3) (ablation)BANMo · Eagledeformation regularityHelped
Use neural blend skinning instead of MLP-translation (ablation)BANMo · Eaglegeometric registrationHelped
Use 16 bones instead of 25 (ablation)BANMo · AMA swing and sambaChamfer distance (cm): 9.08 -> 9.02Helped
Add the in-surface loss (ablation)BANMo · AMA swing and sambaF@2% (%): 54.8 -> 57.0Helped
Use embeddings pre-trained for quadruped animals on humans (ablation)BANMo · AMA swing and sambaF@2% (%): 57.0 -> 54.2Hurt
Increase initial pose error to 90° (ablation)BANMo · AMA swing and sambaChamfer distance (cm): 9.08 -> 9.91Hurt
Increase view coverage from 90° to 180° (ablation)BANMo · AMA swing and sambaF@2% (%): 54.3 -> 59.8Helped

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