ShapeBoost: Boosting Human Shape Estimation with Part-Based
  Parameterization and Clothing-Preserving Augmentation
Siyuan Bian Jiefeng Li Jiasheng Tang Cewu Lu
Accurate human shape recovery from a monocular RGB image is a challenging
task because humans come in different shapes and sizes and wear different
clothes. In this paper, we propose ShapeBoost, a new human shape recovery
framework that achieves pixel-level alignment even for rare body shapes and
high accuracy for people wearing different types of clothes. Unlike previous
approaches that rely on the use of PCA-based shape coefficients, we adopt a new
human shape parameterization that decomposes the human shape into bone lengths
and the mean width of each part slice. This part-based parameterization
technique achieves a balance between flexibility and validity using a
semi-analytical shape reconstruction algorithm. Based on this new
parameterization, a clothing-preserving data augmentation module is proposed to
generate realistic images with diverse body shapes and accurate annotations.
Experimental results show that our method outperforms other state-of-the-art
methods in diverse body shape situations as well as in varied clothing
situations.