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.

提出了 ShapeBoost，一种新的人体形状恢复框架，通过基于部分的参数化技术实现了对不同身形和穿着不同类型衣物的人群进行像素级对齐，提供了在各种身体形状和穿着情况下优于其他现有方法的实验结果。