Using projection mapping enables us to bring virtual worlds into shared physical spaces. In this paper, we present a novel, adaptable and real-time projection mapping system, which supports multiple projectors and high quality rendering of dynamic content on surfaces of complex geometrical shape. Our system allows for smooth blending across multiple projectors using a new optimization framework that simulates the diffuse direct light transport of the physical world to continuously adapt the color output of each projector pixel. We present a real-time solution to this optimization problem using off-the-shelf graphics hardware, depth cameras and projectors. Our approach enables us to move projectors, depth camera or objects while maintaining the correct illumination, in realtime, without the need for markers on the object. It also allows for projectors to be removed or dynamically added, and provides compelling results with only commodity hardware.

Projection-based mixed reality is an effective tool to create immersive visualizations on real-world objects. Its wide range of applications includes art installations, education, stage shows, and advertising. In this work, we enhance a multi-projector system for dynamic projection mapping by handling various physical stray-light effects: interreflection, projector black-level, and environmental light in real time for dynamic scenes. We show how all these effects can be efficiently simulated and accounted for at runtime, resulting in significantly improved projection mapping results. By adding a global optimization step, we can further increase the dynamic range of the projection.

We present a novel approach to mesh deformation that enables simple context sensitive manipulation of 3D geometry. The method is based on locally anisotropic transformations and is extended to global control directions. This allows intuitive directional modeling within an easy to implement framework.The proposed method complements current sculpting paradigms by providing further possibilities for intuitive surface-based editing without the need for additional host geometries. We show the anisotropic deformation to be seamlessly transferable to free boundary parameterization methods, which allows us to solve the hard problem of flattening compression garments in the domain of apparel design.

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We present a complete pipeline for constructing a statistical shape model that is invariant to deviations in the scan pose while encoding the space of human pose and body shape in an efficient manner. A dense cross-parameterization between a large set of high-quality 3D scans is computed using a fast and robust volume aware non-rigid registration method. Our approach uses a novel encoding that automatically decorrelates shape and pose leading to a statistical model that is oblivious under transformations induced by pose. This allows us to efficiently compensate pose variations in captured input data leading to a compact representation for pose as well as body shape. We present a local as well as a global skeletal encoding and compare both approaches. Finally, we analyze the generalization properties and accuracy of our approach against two state-of-the-art methods. We apply our model to the data clustering problem and use it as a prior for non-rigid shape matching.