Abstract Paper pop‐ups are interesting three‐dimensional books that fascinate people of all ages. The design and construction of these pop‐up books however are done manually and require a lot of time and effort. This has led to computer‐assisted or automated tools for designing paper pop‐ups. This paper proposes an approach for automatically converting a 3D model into a multi‐style paper pop‐up. Previous automated approaches have only focused on single‐style pop‐ups, where each is made of a single type of pop‐up mechanisms. In our work, we combine multiple styles in a pop‐up, which is more representative of actual artist's creations. Our method abstracts a 3D model using suitable primitive shapes that both facilitate the formation of the considered pop‐up mechanisms and closely approximate the input model. Each shape is then abstracted using a set of 2D patches that combine to form a valid pop‐up. We define geometric conditions that ensure the validity of the combined pop‐up structures. In addition, our method also employs an image‐based approach for producing the patches to preserve the textures, finer details and important contours of the input model. Finally, our system produces a printable design layout and decides an assembly order for the construction instructions. The feasibility of our results is verified by constructing the actual paper pop‐ups from the designs generated by our system.

Origamic architecture (OA) is a form of papercraft that involves cutting and folding a single sheet of paper to produce a 3D pop-up, and is commonly used to depict architectural structures. Because of the strict geometric and physical constraints, OA design requires considerable skill and effort. In this paper, we present a method to automatically generate an OA design that closely depicts an input 3D model. Our algorithm is guided by a novel set of geometric conditions to guarantee the foldability and stability of the generated pop-ups. The generality of the conditions allows our algorithm to generate valid pop-up structures that are previously not accounted for by other algorithms. Our method takes a novel image-domain approach to convert the input model to an OA design. It performs surface segmentation of the input model in the image domain, and carefully represents each surface with a set of parallel patches. Patches are then modified to make the entire structure foldable and stable. Visual and quantitative comparisons of results have shown our algorithm to be significantly better than the existing methods in the preservation of contours, surfaces, and volume. The designs have also been shown to more closely resemble those created by real artists.

A paper sliceform or lattice-style pop-up is a form of papercraft that uses two sets of parallel paper patches slotted together to make a foldable structure. The structure can be folded flat, as well as fully opened (popped-up) to make the two sets of patches orthogonal to each other. Automatic design of paper sliceforms is still not supported by existing computational models and remains a challenge. We propose novel geometric formulations of valid paper sliceform designs that consider the stability, flat-foldability and physical realizability of the designs. Based on a set of sufficient construction conditions, we also present an automatic algorithm for generating valid sliceform designs that closely depict the given 3D solid models. By approximating the input models using a set of generalized cylinders, our method significantly reduces the search space for stable and flat-foldable sliceforms. To ensure the physical realizability of the designs, the algorithm automatically generates slots or slits on the patches such that no two cycles embedded in two different patches are interlocking each other. This guarantees local pairwise assembility between patches, which is empirically shown to lead to global assembility. Our method has been demonstrated on a number of example models, and the output designs have been successfully made into real paper sliceforms.

In this paper, we propose a novel two-level regression method for computing body mass distribution from a database of X-ray images, without scanning the new subject. Our approach first selects a suitable sample from the image database by minimizing a distance function based on the relationships between the new subject's body measurements and those of sample subjects. The X-ray image of the new subject is then predicted from the sample image using a feature-based transformation. Body mass distribution is computed directly from the predicted X-ray image. Our results surpass the accuracy of commonly used mass distribution regression methods in biomechanics literatures. In addition, by not scanning the new subject, we avoid all the radiation and cost involved in X-ray absorptiometry.

We address the volumetric reconstruction problem that takes as input a series of orthographic multi-energy x-ray images, producing as output a reconstructed model space consisting of uniform-size mass density voxels. Our approach solves the non-linear constrained optimization for mulation problem by constructing a compliant estimate of volumetric distribution, subject to projective and domain constraints, and minimizes variational irregularities. To resolve the inherent ambiguities of single-view formulation, an optional shape model may be introduced to aid the reconstruction process. We demonstrate our method’s practical usage as a new in-vivo method for estimating three dimensional body segmental compositions, and compare its results with those of existing methods.

A fully automatic 3D-2D articulated registration algorithm for aligning a whole-body human geometry to a 2D X-ray image is presented. Domain prior in the form of a hierarchical model encapsulating the kinematic structure of the human body is exploited. A deformation operator is then defined with respect to the hierarchical model. An iterative search using ICP is performed to estimate the optimal deformation parameters. Empirical results indicate good matching rates of 94% and above. Requiring minimal user intervention, the method is suited for large-scale data acquisition in human anthropometric studies.

Despite leaps in motion capture technology, the dichotomy between unencumbered vision-based motion recovery and the prevailing marker-assisted motion capture solution remains largely pertinent. This paper bridges the gap by introducing an attachment-free, full-body motion capture technique that employs multiple high-speed cameras and a customized bodysuit. The flexible bodysuit allows free movements while providing marker data for guided reconstruction of pose and motion. Our method overcomes several practical problems in existing systems, including long preparation time and displaced markers. The network of markerized pattern provides connectivity information and enhances robustness in feature tracking, model matching and 3D reconstruction.