Figure 1: This paper presents a method for automatically constructing 3D meshes from a single piece of concept artwork. This figure shows how a typical 2D concept image (a) is separated from the background (b) and how a bent skeleton (c) is generated and used to create polygonal shells (d). The profile view (e) shows clearly how the shells create a 3D representation of the input image. The final model (f -h) is mapped with textures based on the initial concept artwork and contains bone and vertex weighting information appropriate for animation. Details such as the hair and ears are correctly transformed into 3D, and mechanical objects such as the gun and knife have sharp edges. AbstractIn this paper we present a new method for automatically constructing 3D meshes from a single input image. With the increasing content demands of modern digital entertainment and the expectation of involvement from users, automatic artist-free systems are an important step in allowing user generated content and rapid game prototyping. Our system proposes a novel heuristic for the creation of a 3D mesh from a single piece of non-occluding 2D concept art. By extracting a skeleton structure, approximating the 3D orientation and analysing line curvature properties, appropriate centrepoints can be found around which to create the cross-sectional slices used to build a final triangle mesh. Our results show that a single 2D input image can be used to generate a rigged 3D lowpolygon model suitable for use in realtime applications.

This paper proposes a view dependent rendering setup for home computer use. View dependent rendering uses a parallax effect to give the illusion of depth. Our face tracking method is based on the Lucas-Kanade and Haar algorithms, and runs efficiently so as not to impede other programs. Our setup uses a single webcam to locate the user spatially, allowing a scene to be rendered differently depending upon their 3D location.

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This paper presents a new automatic algorithm for extracting vector information from raster images. The algorithm extracts structural information from the lines that is formatted to allow easy processing and evaluation of the image structure. Vectorization results are comparable with commonly used algorithms, however the outlined method differs from prior work by providing information in a more accessible form. This algorithm provides topological information at the cost of visual fidelity. Properties such as line topology and width are important for image processing, including object decomposition, author recognition and line style modification.