Human plays a key role in metaverse and digital twin systems and thus live three-dimensional (3D) human shape reconstruction brings significant improvement for highly accurate anthropomorphic measurements. However, it is still difficult to obtain detailed 3D computational models or “virtual humans” due to the diversity of individual poses and shape deformation. With technical advantages of the metaverse geometric modeling, virtual characters can be created in seconds. Concurrent, tremendous progress has been achieved in 3D modeling technology and larger representative public human datasets have emerged. In this survey, the status quo of 3D body digitization is investigated, and the accompanying processing pipeline is introduced. Specifically, this survey first discusses mainstream 3D scanning technologies for virtual 3D body acquisition. Second, we investigate the status quo of the commonly-used body measurement processing frameworks from four aspects: 1) data acquisition; 2) feature extraction; 3) surface modeling; and 4) landmarking and digital measurements. Finally, through a comparative analysis of prior works, we highlight challenges and new trends of ongoing developments based on insights into the advantages and limitations of existing works.

Size mismatch is a serious problem in online footwear purchase because size mismatch implies an almost sure return. Not only foot measurements are important in selecting a size, but also user preference. This is the reason we propose several methodologies that combine the information provided by a classifier with anthropometric measurements and user preference information through user-based collaborative filtering. As novelties: (1) the information sources are 3D foot measurements from a low-cost 3D foot digitizer, past purchases and self-reported size; (2) we propose to use an ordinal classifier after imputing missing data with different options based on the use of collaborative filtering; (3) we also propose an ensemble of ordinal classification and collaborative filtering results; and (4) several methodologies based on clustering and archetype analysis are introduced as user-based collaborative filtering for the first time. The hybrid methodologies were tested in a simulation study, and they were also applied to a dataset of Spanish footwear users. The results show that combining the information from both sources predicts the foot size better and the new proposals provide better accuracy than the classic alternatives considered.

Foot measurements can be used in health assessment and product design (e.g. footwear/orthotic personalization or size recommendation). Digital measurements have proven to be more reliable than trad...