Interactive motion generation from examples

TLDR

Many applications require large amounts of natural‑looking motion, yet synthesizing realistic human motion remains difficult. The paper proposes a framework that generates human motions by cutting and pasting motion‑capture data. The framework selects motion clips via a randomized search over a hierarchy of graphs to assemble natural‑looking sequences. The approach automatically generates smooth, human‑looking motion sequences in real time that satisfy diverse constraints, produce multiple interchangeable variants, support interactive authoring, and enable extensive reuse of motion‑capture data.

Abstract

There are many applications that demand large quantities of natural looking motion. It is difficult to synthesize motion that looks natural, particularly when it is people who must move. In this paper, we present a framework that generates human motions by cutting and pasting motion capture data. Selecting a collection of clips that yields an acceptable motion is a combinatorial problem that we manage as a randomized search of a hierarchy of graphs. This approach can generate motion sequences that satisfy a variety of constraints automatically. The motions are smooth and human-looking. They are generated in real time so that we can author complex motions interactively. The algorithm generates multiple motions that satisfy a given set of constraints, allowing a variety of choices for the animator. It can easily synthesize multiple motions that interact with each other using constraints. This framework allows the extensive re-use of motion capture data for new purposes.