We describe a new shape deformation method that uses constraints extracted from a data set of models to streamline the editing process. Many man-made shapes are designed with geometric constraints originating from functional, aesthetic, manufacturing requirements among others. However, these constraints are often non-explicitly available, making shape editing difficult to perform. In this work, we aim to learn such constraints by studying a product family conjointly. Our method is based on a co-constrained abstraction whose surfaces can be used as deformation handles for editing. These handles are generated uniquely for each shape, however the deformations enabled by these handles are determined by a statistical analysis of the shape set. Given a set of segmented models, our method creates an abstraction for each model. It then clusters the individual surfaces in the set of abstract models. From this, we compute the statistical relationships between the individual surfaces of a model and also across the surfaces of different models in the data set. The result is a co-constrained abstraction for each model. The surfaces of these abstract models encode the computed statistical information and serve as handles during shape editing. Co-constrained abstractions are guaranteed to be water-typed. They use a volumetric deformation system designed to preserve shape details. Several input modalities and feedback mechanisms are available for user interaction. Primarily, the user can apply affine transformations to the deformation handles using the individual manipulators. The user can also sketch to control the surface handles that have been cleared for freeform deformation. Additionally, the user may select the level of detail and the set of handles they would like to work with. User feedback happens at multiple levels. First, the handles of the co-constrained abstraction contain cues about the shapes and the data set. Second, the system disables manipulators that will create non-conformal deformations. A visual widget is available that shows the active handles statistics relative to the data set. Third, when the user activates a handle, the system automatically determines the best surface to keep fixed, as well as the ones that would deform. The user can choose to display or hide all such feedback. Here we present several user edits and deformation results. Our system allows arbitrary deformations, which can be constrained by information learned from the set analysis. When desired, the user may overrule such constraints for maximum freedom and deformation, without compromising man-made, shaped, detailed preservation. Using this approach, normally complex deformations can be authored using simple transformations and freeform sketching on the co-constrained abstractions. Our method enables us to automatically apply constrained deformations that modify a model toward the average of the data set. We demonstrate how this can be used for abstract style transfer. Note that airplanes remain nearly undefformed if processed within very similar models. However, when a stylistically different set of aircrafts is used for training, the resulting deformations become much more significant. In this example, an input car can be automatically stylized to look like a classic sedan model, or made to look more sporty based on the data set used for training. Our approach is also useful for simultaneous code deformation of all the models in a data set. A series of user edits recorded and applied to one model can be automatically propagated to all other models in the shape set. We describe a new deformation method that learns the implicit shape constraints underlying a family of shapes using a geometric analysis. We use the computed statistical information to guide the user in shape editing, as well in applications that require geometric registration, such as abstract shape transfer and code deformation. With this work, we hope to make shape deformation technology more streamlined and intuitive for the end users.