Estimation of Human Body Shape and Posture Under Clothing
This work addresses the need for accurate body shape estimation in applications like virtual change rooms and security, representing an incremental improvement over existing statistical models.
The paper tackled the problem of estimating human body shape and posture from clothed 3D scans, proposing a method that uses a posture-invariant shape space and skeleton-based deformation. It achieved higher fitting accuracy compared to a SCAPE model variant, though no specific numbers were provided.
Estimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security. To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate. In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation. Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans. In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates. We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model as statistical model.