We present MotionMontage, a system for recording multiple motion takes of a rigid virtual object and compositing them together into a montage. Our system incorporates a Kinect-based performance capture setup that allows animators to create 3D animations by tracking the motion of a rigid physical object and mapping it in realtime onto a virtual object. The animator then temporally annotates the best parts of each take. MotionMontage merges the annotated motions into a single composite montage using a combination of dynamic time warping and optimization of a Semi-Markov Conditional Random Field. Our system also supports the creation of layered animations in which multiple objects are moving at the same time. To aid the animator in coordinating the motions of the objects we provide spatial markers which indicate the positions of previously recorded objects at user-specified points in time. We perform a user study to evaluate the perceived quality of the montages created with our system and find that viewers (including both the original animators and new viewers) generally prefer the animation montage to any individual take.

We demonstrate a realtime system which infers and tracks the assembly process of a snap-together block model using a Kinect® sensor. The inference enables us to build a virtual replica of the model at every step. Tracking enables us to provide context specific visual feedback on a screen by augmenting the rendered virtual model aligned with the physical model. The system allows users to author a new model and uses the inferred assembly process to guide its recreation by others. We propose a novel way of assembly guidance where the next block to be added is rendered in blinking mode with the tracked virtual model on screen. The system is also able to detect any mistakes made and helps correct them by providing appropriate feedback. We focus on assemblies of Duplo® blocks.

We present a system for producing 3D animations using physical objects (i.e., puppets) as input. Puppeteers can load 3D models of familiar rigid objects, including toys, into our system and use them as puppets for an animation. During a performance, the puppeteer physically manipulates these puppets in front of a Kinect depth sensor. Our system uses a combination of image-feature matching and 3D shape matching to identify and track the physical puppets. It then renders the corresponding 3D models into a virtual set. Our system operates in real time so that the puppeteer can immediately see the resulting animation and make adjustments on the fly. It also provides 6D virtual camera \\rev{and lighting} controls, which the puppeteer can adjust before, during, or after a performance. Finally our system supports layered animations to help puppeteers produce animations in which several characters move at the same time. We demonstrate the accessibility of our system with a variety of animations created by puppeteers with no prior animation experience.