We propose a method that allows an unskilled user to create an accurate physical replica of a digital 3D model. We use a projector/camera pair to scan a work in progress, and project multiple forms of guidance onto the object itself that indicate which areas need more material, which need less, and where any ridges, valleys or depth discontinuities are. The user adjusts the model using the guidance and iterates, making the shape of the physical object approach that of the target 3D model over time. We show how this approach can be used to create a duplicate of an existing object, by scanning the object and using that scan as the target shape. The user is free to make the reproduction at a different scale and out of different materials: we turn a toy car into cake. We extend the technique to support replicating a sequence of models to create stop-motion video. We demonstrate an end-to-end system in which real-world performance capture data is retargeted to claymation. Our approach allows users to easily and accurately create complex shapes, and naturally supports a large range of materials and model sizes.

Many kinds of digital fabrication are accomplished by precisely moving a tool along a digitally-specified path. This precise motion is typically accomplished fully automatically using a computer-controlled multi-axis stage. With that approach, one can only create objects smaller than the positioning stage, and large stages can be quite expensive. We propose a new approach to precise positioning of a tool that combines manual and automatic positioning: in our approach, the user coarsely positions a frame containing the tool in an approximation of the desired path, while the device tracks the frame's location and adjusts the position of the tool within the frame to correct the user's positioning error in real time. Because the automatic positioning need only cover the range of the human's positioning error, this frame can be small and inexpensive, and because the human has unlimited range, such a frame can be used to precisely position tools over an unlimited range.

We present a new sketch-based modeling approach in which models are interactively designed by drawing their 2D silhouettes from different views. The core idea of our paper is to limit the input to 2D silhouettes, removing the need to explicitly create or position 3D elements. Arbitrarily complex models can be constructed by assembling them out of parts defined by their silhouettes, which can be combined using CSG operations. We introduce a new simplified algorithm to compute CSG solids that leverages special properties of silhouette cylinders to convert the 3D CSG problem into one that can be handled entirely with 2D operations, making implementation simpler and more robust. We evaluate our approach by modeling a random sampling of man-made objects taken from the words in WordNet, and show that all of the tested man-made objects can be modeled quickly and easily using our approach.

We present a way to bring cartoon objects and characters into the third dimension, by giving them the ability to rotate and be viewed from any angle. We show how 2D vector art drawings of a cartoon from different views can be used to generate a novel structure, the 2.5D cartoon model, which can be used to simulate 3D rotations and generate plausible renderings of the cartoon from any view. 2.5D cartoon models are easier to create than a full 3D model, and retain the 2D nature of hand-drawn vector art, supporting a wide range of stylizations that need not correspond to any real 3D shape.

We introduce a simple technique that enables robust approximation of volumetric, large-deformation dynamics for real-time or large-scale offline simulations. We propose Lattice Shape Matching , an extension of deformable shape matching to regular lattices with embedded geometry; lattice vertices are smoothed by convolution of rigid shape matching operators on local lattice regions, with the effective mechanical stiffness specified by the amount of smoothing via region width. Since the naïve method can be very slow for stiff models - per-vertex costs scale cubically with region width - we provide a fast summation algorithm, Fast Lattice Shape Matching (FastLSM) , that exploits the inherent summation redundancy of shape matching and can provide large-region matching at constant per-vertex cost. With this approach, large lattices can be simulated in linear time. We present several examples and benchmarks of an efficient CPU implementation, including many dozens of soft bodies simulated at real-time rates on a typical desktop machine.