Geometric morphometrics has become an essential tool for the quantitative characterization of complex phenotypes. In the past 20 years, morphometric approaches have been used to study phenotypic plasticity, to test different models of quantitative trait evolution, to infer modularity and integration, to study changes in ontogenetic development, among others. Consequently, morphometric research has undergone rapid development from an analytical standpoint.Despite these developments, the gold standard for landmark data collection has remained largely the same. Morphometric data is, by and large, manually digitized by experts. Manual digitization of landmarks is, however, both low-throughput and subject to a significant amount of inter-observer bias, representing, therefore, an important barrier to further advances in the field.In this talk, I will describe a new and general framework for automated landmarking of 3D biological structures called ALPACA (Automated Landmarking through Pointcloud Alignment and Correspondence Analysis). ALPACA approaches the problem of automated landmarking using deformable pointcloud registration. In short, a reference mesh (the source mesh) is subsampled, aligned and posteriorly deformed to match a target mesh. Using the transformation parameters used to deform one mesh into another, we project the landmark positions of the source mesh into the target one. Given the recent explosion in the availability of 3D datasets in ecology and evolutionary biology, we expect this method to have broad appeal to researchers in the field.