We've put up a project page and a preprint of our new SIGGRAPH 2016 paper "Rig Animation with a Tangible and Modular Input Device", joint work with Glauser Oliver, Wan-Chun Ma, Daniele Panozzo, O. Hilliges, O. Sorkine-Hornung.
This is not just version 2.0 of our tangible and modular input device from 2014 (although the new hardware is totally awesome). In this paper we also present a new optimization for mapping joints and splitters to any industry-grade character rig. The optimization will output instructions for a device to construct out of parts and then map those degrees of freedom to all parameters of the rig.
Abstract We propose a novel approach to digital character animation, combining the benefits of tangible input devices and sophisticated rig animation algorithms. A symbiotic software and hardware approach facilitates the animation process for novice and expert users alike. We overcome limitations inherent to all previous tangible devices by allowing users to directly control complex rigs using only a small set (5-10) of physical controls. This avoids oversimplification of the pose space and excessively bulky device configurations. Our algorithm derives a small device configuration from complex character rigs, often containing hundreds of degrees of freedom, and a set of sparse sample poses. Importantly, only the most influential degrees of freedom are controlled directly, yet detailed motion is preserved based on a pose interpolation technique. We designed a modular collection of joints and splitters, which can be assembled to represent a wide variety of skeletons. Each joint piece combines a universal joint and two twisting elements, allowing to accurately sense its configuration. The mechanical design provides a smooth inverse kinematics-like user experience and is not prone to gimbal locking. We integrate our method with the professional 3D software Autodesk Maya® and discuss a variety of results created with characters available online. Comparative user experiments show significant improvements over the closest state-of-the-art in terms of accuracy and time in a keyframe posing task.