Manually assembling such arrangements in virtual scenes is highly time consuming, especially when one needs to generate multiple diverse arrangements for numerous support surfaces and living spaces. The input to our method is a wire sculpture design. We further demonstrate a compelling gallery of 3D character canvases created from a diverse set of cartoon drawings with matching 3D skeletons. The algorithms she developed based on these insights help turn diverse shapes like airplanes, cars, coffee makers and mugs into sketches. We present a geometric representation of a tetrahedral mesh that is solely based on dihedral angles.
To this end, we introduce a cross-structural element compatibility metric that estimates the impact of each operation on the edited shape. We first segment the input 2D contours into individual body-part outlines corresponding to 3D skeletal bones using the Gestalt continuation principle to correctly resolve inter-part occlusions in the drawings. We then use this segmentation to compute the canvas geometry, generating 3D generalized surfaces of revolution around the skeletal bones that conform to the original outlines and balance simplicity against contour persistence. Instead of relying on quantitative distances, which may become unreliable between dissimilar shapes, we introduce a qualitative analysis which utilizes multiple distance measures but only in cases where the measures can be reliably compared. Studies indicate that viewers apply these properties selectively to envision a globally consistent 3D shape.
Our analysis is based on the notion of quartets, each defined by two pairs of shapes, where the shapes in each pair are close to each other, but far apart from the shapes in the other pair. The output is the part label for each capsule. We first employ a graph-cut segmentation technique to extract spatially and temporally reliable surface motion patterns, detecting consistent compressing, stable, and stretching patches. It is very challenging to recover the bare earth, i. We observe that artists are skilled at quickly and effectively conveying poses using such drawings, and design them to facilitate a single perceptually consistent pose interpretation by viewers. The graph structure is obtained by triangulating the point clouds.
Figure 1: Image contours are used to automatically construct a corresponding 3D model with the help of a 3D template. We introduce a novel technique for the construction of a 3D character proxy, or canvas, directly from a 2D cartoon drawing and a user-provided correspondingly posed 3D skeleton. We further validate our algorithm by comparing it against previous work, and demonstrate a significant improvement in both worst and average element quality. Matchmaker allows users to introduce scores of constraints while maintaining a valid one-to-one mapping between the embedding and the 3D surface. It moves the constrained vertices to the required positions by matching a triangulation of these positions to a triangulation of the planar mesh formed by paths between constrained vertices. Extensive testing shows that the recovered surfaces agree very well with those reconstructed from manually corrected data. The technique gradually deforms the template to fit the contours.
We validate our approach via result comparisons to artist-posed models generated from the same reference drawings, via studies that confirm that our results agree with viewer perception, and via comparison to algorithmic alternatives. In contrast, we introduce the first structure-transcending style similarity measure and validate it to be well aligned with human perception of stylistic similarity. Our algorithm leverages perceptual cues to parse the drawings and recover the artist-intended poses. We train and validate our method on this dataset, showing it to successfully predict relative style similarity with near 90% accuracy based on 10-fold cross-validation. Given a shape collection and a user-specified region label our goal is to correctly demarcate the corresponding regions with minimal manual work. Her work seeks to expand the accessibility of shape design for advanced manufacturing techniques, a research priority identified by the Canadian federal government. We propose a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations.
. We mimic this selective regularization algorithmically, by progressively detecting and enforcing applicable properties, accounting for their global impact on an evolving 3D curve network. My research interests are applying computational optimization and numerical simulation to visual computing problems, especially in geometry processing and physics-based animation. The sketches and 3D curves produced by the algorithm were deemed comparable to those produced by professional designers. This tree satisfies the topological qualitative constraints imposed by the quartets creating an effective organization of the shapes. The technique can successfully deform the template to match contours that represent significant changes in shape.
Abstract: A method for generating a polycube representation of an input object comprises: receiving an input volumetric representation of the input object; deforming the input volumetric representation to provide a deformed object representation; and extracting, by the processor, a polycube representation of the object from the deformed object representation. By combining these diverse cues we achieve higher accuracy than existing alternatives. Dynamic folds and wrinkles are an important visual cue for creating believably dressed characters in virtual environments. We validate our algorithm via a range of user studies and comparisons to ground-truth 3D models and artist-drawn results. We exhibit a wide variety of character models posed by our method created from gesture drawings of complex poses, including poses with occlusions and foreshortening.
Moreover, the resulting classification of points is competitive with the best in the literature. Moreover, although compression is predominant on our examples, dilatation still plays an important role, especially for the beret and puff sleeve examples where the natural shape needs to be inflated to account for the target. We present categorization trees computed on various collections of shapes and compare them to ground truth data from human categorization. When the user scrolls in any direction or changes the zoom level, the new cells of the grid that enter the screen space are dynamically filled with shapes based on the similarity graph. We propose the use of a hidden Markov model for efficiently computing an op-timal set of correspondences. Recent literature point to the presence of similarly shaped, salient geometric elements as a main indicator of stylistic similarity between 3D shapes.