Three-dimensional human shape inference from silhouettes: reconstruction and validation

Silhouettes are robust image features that provide considerable evidence about the three-dimensional (3D) shape of a human body. The information they provide is, however, incomplete and prior knowledge has to be integrated to reconstruction algorithms in order to obtain realistic body models. This paper presents a method that integrates both geometric and statistical priors to reconstruct the shape of a subject assuming a standardized posture from a frontal and a lateral silhouette. The method is comprised of three successive steps. First, a non-linear function that connects the silhouette appearances and the body shapes is learnt and used to create a first approximation. Then, the body shape is deformed globally along the principal directions of the population (obtained by performing principal component analysis over 359 subjects) to follow the contours of the silhouettes. Finally, the body shape is deformed locally to ensure it fits the input silhouettes as well as possible. Experimental results showed a mean absolute 3D error of 8 mm with ideal silhouettes extraction. Furthermore, experiments on body measurements (circumferences or distances between two points on the body) resulted in a mean error of 11 mm.