Learnt Real-time Meshless Simulation

Abstract

We present a new real-time approach to simulate deformable objects using a learnt statistical model to achieve a high degree of realism. Our approach improves upon state-of-the-art interactive shape-matching meshless simulation methods by not only capturing important nuances of an object's kinematics but also of its dynamic texture variation. We are able to achieve this in an automated pipeline from data capture to simulation. Our system allows for the capture of idiosyncratic characteristics of an object's dynamics which for many simulations (e.g. facial animation) is essential. We allow for the plausible simulation of mechanically complex objects without knowledge of their inner workings. The main idea of our approach is to use a flexible statistical model to achieve a geometrically-driven simulation that allows for arbitrarily complex yet easily learned deformations while at the same time preserving the desirable properties (stability, speed and memory efficiency) of current shape-matching simulation systems. The principal advantage of our approach is the ease with which a pseudo-mechanical model can be learned from 3D scanner data to yield realistic animation. We present examples of non-trivial biomechanical objects simulated on a desktop machine in real-time, demonstrating superior realism over current geometrically motivated simulation techniques.