![]() ![]() We train and evaluate the CNN in four scenarios: (1) training with subjects of a specific gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera distance. We evaluate the network inference absolute and relative mean error between the estimated and actual HBDs. To that end, we augment our recently published neural anthropometer dataset by rendering images with different camera distance. Our contribution lies in assessing a CNN estimation performance in a series of controlled experiments. Despite the community's tremendous effort to advance human shape analysis, there is a lack of systematic experiments to assess CNNs estimation of human body dimensions from images. ![]() More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multi-task regression problem that can be tackled with machine learning techniques, particularly convolutional neural networks (CNN). ![]() Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). Not All Body Scanning Measurements Are Valid: Perspectives from Pattern Practice. Key landmarks and measurements are identified and this research shows how body scanning technology can be developed to support existing and developing methods of pattern development. This research shows that there are a range of measurements used for pattern making and these are not all available from existing body scanning systems. As well as understanding the suitability of scan measurements for pattern development, this research also recommends further measurements which may improve the patterns’ ability to accord with the individual size, shape and proportion of the wearer. Whilst there are promising developments in automated pattern creation, , there is little existing theory or clear understanding of pattern to person relationships to enable the full realization and embedding of these systems. Further analysis was made regarding the development of custom measurements for each scan system, to see if extra measurements could be defined to match those required or enhance the data used to drive the draft process. Six methods of pattern development, established from previous research to represent the variation of approaches – were selected, the measurements required for these methods were compared to measurement outputs from both a Size Stream and 2 body scanner. This research began with analysis of product development practices and body scanning outputs to determine the suitability of body scanning to support existing methods of product development. The application of body scanning has largely focused on sizing surveys, the standards used in developing the technology are tailored toward surveys, and subsequently measurements are often not defined in a manner suitable to developing products. While human measurement forms the basis for product development and body scanning represents a significant development in the collection of human measurements, a distinction must be drawn between measurements suitable for product development (pattern cutting) and those required for the creation of sizing systems. To assume all body scanning measurements are valid for apparel product development is wrong. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |