A Simple Strategy for Body Estimation from Partial-View Images

Abstract

Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation. Although previous research has advanced in estimating 3D body shapes from RGB images, the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unknown factors: capture distance and body dimensions. This ambiguity is particularly pronounced in partial-view scenarios. To address this challenge, we propose a modular and simple height normalization solution. This solution relocates the subject skeleton to the desired position, thereby normalizing the scale and disentangling the relationship between the two variables. Our experimental results demonstrate that integrating this technique into state-of-the-art human mesh reconstruction models significantly enhances partial body measurement estimation. Additionally, we illustrate the applicability of this approach to multi-view settings, showcasing its versatility.