Body condition is of great interest for ecologists and conservation biologists, because of its strong correlation with many animal traits critical to survival, such as migratory ability, reproduction, and immune function, among others. Current methods for studying body condition are limited such that they tend to involve some form of capture which is expensive and dangerous. The use of visual scoring to approximate fat deposits is an emerging approach for evaluating body condition, but is limited by its subjectiveness, interobserver bias, and the need for professional evaluation. In this study, we obtained a 3D of a polar bear (Ursus marimitus) through photogrammetric capture, and modified the 3D model using Blender software to make it capable of simulating body condition on a continuous scale. Furthermore, we generated data using (N = 39) Polar Bears. Our four graphs show strong positive correlations, Dorsal Area vs Volume (R2 = 0.907), Dorsal Area vs Surface Area (R2 = 0.907), Lateral Area vs Volume (R2 = 0.989), and Lateral Area vs Surface Area (R2 = 0.988). The use of our method is quantitative, more objective than visual scoring, less expensive, safer, requires less expertise, and has potential for automation with future work using machine learning algorithms.