Mapping the motion of an actor's face to a virtual model is a difficult but important problem, especially as fully animated characters are becoming more common in games and movies. Many methods have been proposed but most require the source and target to be structurally similar. Optical motion capture markers and blendshape weights are an example of topologically incongruous source and target examples that do not have a simple mapping between one another. In this paper, we created a system capable of determining this mapping through supervised learning of a small training dataset. Radial Basis Function Networks (RBFNs) have been used for retargeting markers to blendshape weights before but to our knowledge Multi-Layer Perceptron Artificial Neural Networks (referred to as ANNs) have not been employed in this way. We hypothesized that ANNs would result in a superior retargeting solution compared to the RBFN, due to their theoretically greater representational power. We implemented a retargeting system using ANNs and RBFNs for comparison. Our results found that both systems produced similar results (figure 1) and in some cases the ANN proved to be more expressive although the ANN was more difficult to work with.

Urban models are key to navigation, architecture and entertainment. Apart from visualizing facades, a number of tedious tasks remain largely manual (e.g. compression, generating new facade designs and structurally comparing facades for classification, retrieval and clustering). We propose a novel procedural modelling method to automatically learn a grammar from a set of facades, generate new facade instances and compare facades. To deal with the difficulty of grammatical inference, we reformulate the problem. Instead of inferring a compromising, one-size-fits-all, single grammar for all tasks, we infer a model whose successive refinements are production rules tailored for each task. We demonstrate our automatic rule inference on datasets of two different architectural styles. Our method supercedes manual expert work and cuts the time required to build a procedural model of a facade from several days to a few milliseconds.

Image completion is an important photo-editing task which involves synthetically filling a hole in the image such that the image still appears natural. State-of-the-art image completion methods work by searching for patches in the image that fit well in the hole region. Our key insight is that image patches remain natural under a variety of transformations (such as scale, rotation and brightness change), and it is important to exploit this. We propose and investigate the use of different optimisation methods to search for the best patches and their respective transformations for producing consistent, improved completions. Experiments on a number of challenging problem instances demonstrate that our methods outperform state-of-the-art techniques. © 2011. The copyright of this document resides with its authors.

Improvement in acquisition systems, has resulted in the ability to capture more realistic 3D models of real world objects, creating a need for better data processing techniques, as in the case of text and images. In this paper, we address the issue of learning class-specific, deformable, 3D part-based structure for object part localization in 3D models/scenes. We employ an inference framework upon fully connected part-based graphs inspired by Pictorial Structures (PS), which combine the local appearance of parts and the long-range structural properties. Using efficient tools for learning the model and performing inference, we show good results on a variety of classes, outperforming PS [7] and ISM [15]. Further, a similar inference framework is employed to find dense correspondences between 3D models, seeded by the above object part localization. Our results show promise for application in more complex 3D processing tasks such as part retrieval, pose estimation, scene understanding and recognition. © 2011. The copyright of this document resides with its authors.

An image search for “clownfish” yields many photos of clownfish, each of a different individual of a different 3D shape in a different pose. Yet, to the human observer, this set of images contains enough information to infer the underlying 3D deformable object class. Our goal is to recover such a deformable object class model directly from unordered images. For classes where feature-point correspondences can be found, this is a straightforward extension of non-rigid factorization, yielding a set of 3D basis shapes to explain the 2D data. However, when each image is of a different object instance, surface texture is generally unique to each individual, and does not give rise to usable image point correspondences. We overcome this sparsity using curve correspondences (crease-edge silhouettes or class-specific internal texture edges). Even rigid contour reconstruction is difficult due to the lack of reliable correspondences. We incorporate correspondence variation into the optimization, thereby extending contour-based reconstruction techniques to deformable object modelling. The notion of correspondence is extended to include mappings between 2D image curves and corresponding parts of the desired 3D object surface. Combined with class-specific priors, our method enables effective de-formable class reconstruction from unordered images, despite significant occlusion and the scarcity of shared 2D image features.

Recent advances in single-view reconstruction (SVR) have been in modelling power (curved 2.5D surfaces) and automation (automatic photo pop-up). We extend SVR along both of these directions. We increase modelling power in several ways: (i) We represent general 3D surfaces, rather than 2.5D Monge patches; (ii) We describe a closed-form method to reconstruct a smooth surface from its image apparent contour, including multilocal singularities ("kidney-bean" self-occlusions); (iii) We show how to incorporate user-specified data such as surface normals, interpolation and approximation constraints; (iv) We show how this algorithm can be adapted to deal with surfaces of arbitrary genus. We also show how the modelling process can be automated for simple object shapes and views, using a-priori object class information. We demonstrate these advances on natural images drawn from a number of object classes.