Diverse Motions and Character Shapes for Simulated Skills

Abstract

We present an optimization framework that produces a diverse range of motions for physics-based characters for tasks such as jumps, flips, and walks. This stands in contrast to the more common use of optimization to produce a single optimal motion. The solutions can be optimized to achieve motion diversity or diversity in the proportions of the simulated characters. As input, the method takes a character model, a parameterized controller for a successful motion instance, a set of constraints that should be preserved, and a pairwise distance metric. An offline optimization then produces a highly diverse set of motion styles or, alternatively, motions that are adapted to a diverse range of character shapes. We demonstrate results for a variety of 2D and 3D physics-based motions, showing that the approach can generate compelling new variations of simulated skills.