Abstract Today, an increasing number of applications in domains such as cultural heritage, healthcare, education, entertainment, and fashion require high-fidelity 3D avatars. However, generating avatars that faithfully reproduce users’ bodies through modeling or acquisition techniques remains challenging and time-consuming, particularly in applications where the accurate quantitative reproduction of body shape and precise anthropometric measurements is required. Thus, attention is shifting towards machine learning-based approaches, in particular those able to fit a parametric model representing the avatar to the intended body shape. Among these models, the Sparse Unified Part-Based Human Representation (SUPR) has been proven to offer superior performance compared to other representations. However, its adoption is primarily hindered by the lack of datasets built upon it. This paper addresses this gap by proposing BOdy shape parameter and 3D meshes of Individuals basEd on SUPR (BODIES) , a dataset containing 84,000 synthetic-generated subjects described using the SUPR model with different numbers of parameters. The paper also presents the results of three experimental studies aimed at assessing the improvements brought by the SUPR model over the state-of-the-art when used to feed an existing framework for generating 3D avatar meshes.
Cannavò et al. (Tue,) studied this question.