Understanding the three-dimensional (3D) structure of animals and humans from everyday images and videos is essential for a wide range of real-world applications - from analyzing animal motion in biological research to planning surgeries for children with variations in hand anatomy. This project supports the development of a new class of digital shape models capable of accurately representing deformable objects like animal bodies and human hands, even when their internal skeletal structures deviate from the norm. Unlike existing models that rely on a fixed skeleton, this project enables adaptive, learnable models that can accommodate diverse and atypical anatomies. By supporting flexible modeling across a broad spectrum of species and conditions, this work has the potential to advance research in biology, medicine, and education. The project includes plans for public release of tools and datasets, along with educational outreach involving students and domain experts. The research will develop a universal deformable shape modeling framework that integrates data from 3D scans, images, and videos to handle objects with varied skeletal topologies. The research includes three main thrusts: (1) learning mesh-based and implicit shape generators with disentangled latent representations for object type, shape, and pose; (2) constructing bone-driven shape priors that generalize to previously unseen skeletal structures, enabling modeling of rare or pathological forms; and (3) applyin