In the past 15 years impressive advances have been made in capturing, modeling and tracking the human body. Animals have received much less attention, despite many applications in biomechanics, biology, neuroscience, robotics, and entertainment. The main reason for the lack of 3D animal models is that the methods for modeling the human body cannot be easily applied to animals: animals are not cooperative, cannot be brought to the lab in large numbers, and current scanners cannot be taken into the wild. Additionally they vary significantly in shape and even in the type of body parts they have.
In this project we develop methods to learn 3D articulated statistical shape models that can represent a wide variety of species in the animal kingdom, allowing intra- and inter-species analysis of 3D shape and the automatic and non-invasive assessment of animal shape from images.
From scans of toy animals, we learn the SMAL (Skinned Multi Animal Linear) model [ ], a 3D articulated statistical shape model able to represent animal shapes for different species: big cats, dogs, cows, horses, zebras, and hippos. To capture animals outside the SMAL space, we developed SMALR (SMAL with Refinement) [ ]. SMALR estimates a detailed 3D textured mesh using a small set of uncalibrated, non-simultaneous images of the animal.
Today animal motion is mostly captured indoors for domestic species with marker-based systems. To address this we are exploiting our 3D articulated animal shape models to develop a markerless motion capture system that will capture the shape and articulated motion of wild animals in their natural environment.