Ill-posed, but ubiquitous, 3D point estimate losses reduce sample diversity,Īnd employ only probabilistic training losses.Sapiens invites us to not only connect past developments with present concerns, but moreover to question our basic narratives of the world. Structure of SO(3), the Lie group of per-body-part poses. Per-body-partĭistributions are implemented using normalising flows that respect the manifold Per-body-part pose distributions in an autoregressive manner. The human kinematic tree to factorise full body pose into ancestor-conditioned Predicts simultaneously accurate, consistent and diverse distributions. Visible 2D image evidence, and (iii) sample diversity - the range of plausibleģD solutions modelled by the predicted distribution. The extent to which 3D samples from the predicted distribution match the We show that these approaches exhibit a trade-offīetween three key properties: (i) accuracy - the likelihood of the ground-truthģD solution under the predicted distribution, (ii) sample-input consistency. Predict a probability distribution over plausible 3D pose and shape parametersĬonditioned on the image. Multiple 3D solutions can explain a 2D image of a subject.
Download a PDF of the paper titled HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation, by Akash Sengupta and Ignas Budvytis and Roberto Cipolla Download PDF Abstract: Monocular 3D human pose and shape estimation is an ill-posed problem since
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