# Measurement-Driven Synthesis of Female Digital Mannequin Using Convex Sub-Volumes
## 2 Literature Review
### Non-Convex Example-Based Modeling
#### 2.1.2 Synthesis through Multiple Example Models  
References [2; 17; 18; 19; 20; 21; 22; 23; 24; 25] extract _features_ from sets of 3D scans of different individuals and correlate their variations in size and shape to each of their measurements. With such correlation, new individuals can be produced based on input measurements.  
* Reference [2] proposes the creation of a database from scanned models. Correlations between the meshes (feature points and curves) and semantic parameters are created and computed as a linear system of equations. The system of equations has its complexity reduced through PCA. New models are produced by modifying input models or finding and modifying similar models (from databases) to satisfy input measurements.
* References [17; 18] preprocess 250 (125 females and 125 males) scanned models from the _SizeKorea_ database. The models are categorized, and the correlations between body shape variation and body sizes are extracted through statistical analysis (PCA). With such correlations, new models are obtained through shape parameter optimization techniques and radial-basis function (RBF) deformation of a template surface.
* Reference [19] obtains a set of thousands of scans, for which they extract and correct landmarks. Then, PCA is performed to extract the parameters that define shape variation, which are stored in a database. Moreover, a system in which users can have access to individual 3D scanned data and personalized body-shape modeling is presented.
* Reference [20] presents a unified model that describes pose, shape, and muscular deformation. A set of 550 full-body scans of 114 subjects is obtained, registered, and encoded. Rotation-invariant encoding allows for the creation of semantic regression functions (PCA-based technique). The regression functions are used to generate arbitrary models.
* Reference [21] constructs a database of 160 (80 male and 80 female) body scans. PCA is then performed to parameterize the data and characterize the tendency of shape variation and modeling parameters, and their correlation with anthropometric parameters. Through such correlations and a non-linear error-optimization-based shape modeling method, an arbitrary model can be obtained from user-specified measurements.
* Reference [22] extracts feature points from a set of scanned models. The feature points are used to create feature curves, which are then parameterized. Using a numerical optimization-based scheme, a new model can be synthesized by interpolating certain examples from the database. These examples are selected through an optimization algorithm.
* Reference [23] proposes a learning-based method to define the relationship between feature curves (wireframes) and anthropometric measurements. Such a relationship is built using a deep neural network (DNN) based on a test dataset and used to build new wireframes. New meshes can be created by interpolating the new wireframes into patches.
* Reference [24] proposes a modeling scheme called SMPL, which uses 2100 female models from the CAESAR project database to train a set of parameters. Such parameters are used in linear blending functions (i.e., shape, skinning, and pose) and a regressor to reconstruct a new mesh from the vertex of a template mesh.  
* Reference [25] proposes the extraction of measurements from 1024 bodies using VR controllers (i.e., HTC Vive). These measurements are then related to the SIMPL [24] model shape parameters. With such relations, the author train four regressors that allow the creation of new meshes from body measurements obtained with the VR controllers.