| Of all the options available to computer animators, physics engines are typically used to develop animations where realism is important. When simulating deformable bodies animators first model the object and then find the object's material parameters in a manual, iterative manner which is both inaccurate and time consuming. This thesis defines a method for finding the physics engine coefficients of an object by examining recordings of the object under various stress tests. It also investigates how these coefficients are coupled to an object's geometry by applying them to 3D models with very different geometries. To accomplish these, this thesis defines a set of deterministic experiments to put various types of stress on the object, develops a computer vision algorithm which takes the recordings and extracts the important behavioral information, develops a simulation of the object under the same type of stress using the bullet physics engine, and defines a feed-back loop algorithm for optimizing the simulation until it matches the criteria set by the extracted behavioral information. This thesis then uses the coefficients obtained to simulate an object with a different geometry.;Using color and shape cues of the recorded experiments, this thesis is successful in defining novel methods for extracting the size and location of the object over time. The framework for simulating the object, while it is still in infancy, has an algorithm which successfully creates a homogenous, isotropic version of the object mimicking the global behavior and local deformation of the recorded. While future work would improve the results, this thesis has successfully shown that extracting material properties from a recorded video (recorded under a strict experimental environment) and using that information to data-drive a simulation of that object is achievable and produces good results. |
