We present a multi-sensor system for consistent 3D hand pose tracking and modeling that leverages the advantages of both wearable and optical sensors. Specifically, we employ a stretch-sensing soft glove and three IMUs in combination with an RGB-D camera. Different sensor modalities are fused based on the availability and confidence estimation, enabling seamless hand tracking in challenging environments with partial or even complete occlusion. To maximize the accuracy while maintaining high ease-of-use, we propose an automated user calibration that uses the RGB-D camera data to refine both the glove mapping model and the multi-IMU system parameters. Extensive experiments show that our setup outperforms the wearable-only approaches when the hand is in the field-of-view and outplays the camera-only methods when the hand is occluded.

We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line-of-sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive layers with complex geometries into silicone compounds. Overlapping conductive areas from separate layers form local capacitors that measure dense area changes. Contrary to existing fabrication methods, the proposed technique only requires hardware that is readily available in modern fablabs. While area measurements alone are not enough to reconstruct the full 3D deformation of a surface, they become sufficient when paired with a data-driven prior. A novel semi-automatic tracking algorithm, based on an elastic surface geometry deformation, allows us to capture ground-truth data with an optical mocap system, even under heavy occlusions or partially unobservable markers. The resulting dataset is used to train a regressor based on deep neural networks, directly mapping the area readings to global positions of surface vertices. We demonstrate the flexibility and accuracy of the proposed hardware and software in a series of controlled experiments and design a prototype of wearable wrist, elbow, and biceps sensors, which do not require line-of-sight and can be worn below regular clothing.

We propose a stretch-sensing soft glove to interactively capture hand poses with high accuracy and without requiring an external optical setup. We demonstrate how our device can be fabricated and calibrated at low cost, using simple tools available in most fabrication labs. To reconstruct the pose from the capacitive sensors embedded in the glove, we propose a deep network architecture that exploits the spatial layout of the sensor itself. The network is trained only once, using an inexpensive off-the-shelf hand pose reconstruction system to gather the training data. The per-user calibration is then performed on-the-fly using only the glove. The glove's capabilities are demonstrated in a series of ablative experiments, exploring different models and calibration methods. Comparing against commercial data gloves, we achieve a 35% improvement in reconstruction accuracy.

In this video we present soft self-sensing capacitive arrays and demonstrate their use in capturing dense surface deformations without requiring line of sight. The capacitive arrays are made of two electrode patterns embedded into a single silicone compound. The overlaps of the electrode strip patterns form local capacitors. As the sensor is stretched the local capacitance measurements change. We introduce a fabrication technique that allows to produce such sensors while only requiring hardware readily available in modern fablabs. The resulting sensors are able to densely capture area changes as they deform. Since they do not directly measure bend, a prior is required to fully reconstruct the underlying motion. We propose a deep neural network regressing the sensor geometry from the local area measurements. A motion capture system is used for training data acquisition. At runtime vertex positions are predicted and used as positional constraints to deform a mesh using a state-of-the-art elastic surface energy. The flexibility and accuracy of the introduced sensors is demonstrated in a series of controlled experiments and by fabricating a prototype sensor and applying it to capture deforming skeletal and non-skeletal objects.

Abstract Laser cutting is an appealing fabrication process due to the low cost of materials and extremely fast fabrication. However, the design space afforded by laser cutting is limited, since only flat panels can be cut. Previous methods for manufacturing from flat sheets usually roughly approximate 3D objects by polyhedrons or cross sections. Computational design methods for connecting, interlocking, or folding several laser cut panels have been introduced; to obtain a good approximation, these methods require numerous parts and long assembly times. In this paper, we propose a radically different approach: Our approximation is based on cutting thin, planar spirals out of flat panels. When such spirals are pulled apart, they take on the shape of a 3D spring whose contours are similar to the input object. We devise an optimization problem that aims to minimize the number of required parts, thus reducing costs and fabrication time, while at the same time ensuring that the resulting spring mimics the shape of the original object. In addition to rapid fabrication and assembly, our method enables compact packaging and storage as flat parts. We also demonstrate its use for creating armatures for sculptures and moulds for filling, with potential applications in architecture or construction.

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.

Articulation of 3D characters requires control over many degrees of freedom: a difficult task with standard 2D interfaces. We present a tangible input device composed of interchangeable, hot-pluggable parts. Embedded sensors measure the device's pose at rates suitable for real-time editing and animation. Splitter parts allow branching to accommodate any skeletal tree. During assembly, the device recognizes topological changes as individual parts or pre-assembled subtrees are plugged and unplugged. A novel semi-automatic registration approach helps the user quickly map the device's degrees of freedom to a virtual skeleton inside the character. User studies report favorable comparisons to mouse and keyboard interfaces for the tasks of target acquisition and pose replication. Our device provides input for character rigging and automatic weight computation, direct skeletal deformation, interaction with physical simulations, and handle-based variational geometric modeling.