We present a local joint-constraint model for a single joint which is based on distance fields. Our model is fast, general, and well suited for modeling human joints. In this work, we take a geometric approach and model the geometry of the boundary of the feasible region, i.e., the boundary of all allowed poses. A region of feasible poses can be built by embedding motion captured data points in a signed distance field. The only assumption is that the feasible poses form a single connected set of angular values. We show how signed distance fields can be used to generate fast and general joint-constraint models for kinematic figures. Our model is compared to existing joint-constraint models, both in terms of generality and computational cost.
The presented method supports joint-constraints of up to three degrees of freedom and works well with sampled motion data. Our model can be extended to handle inter-joint dependencies, or joints with more than three degrees of freedom. The resolution of the joint-constraints can be tweaked individually for each degree of freedom, which can be used to optimize memory usage. We perform a comparative study of the key-properties of various joint-constraint models, as well as a performance study of our model compared to the fastest alternative, the box limit model. The study is performed on the shoulder joint, using a motion captured jumping motion as reference.