Vectorization provides a link between raster scans of pencil-andpaper drawings and modern digital processing algorithms that require accurate vector representations. Even when input drawings are comprised of clean, crisp lines, inherent ambiguities near junctions make vectorization deceptively difficult. As a consequence, current vectorization approaches often fail to faithfully capture the junctions of drawn strokes. We propose a vectorization algorithm specialized for clean line drawings that analyzes the drawing's topology in order to overcome junction ambiguities. A gradientbased pixel clustering technique facilitates topology computation. This topological information is exploited during centerline extraction by a new "reverse drawing" procedure that reconstructs all possible drawing states prior to the creation of a junction and then selects the most likely stroke configuration. For cases where the automatic result does not match the artist's interpretation, our drawing analysis enables an efficient user interface to easily adjust the junction location. We demonstrate results on professional examples and evaluate the vectorization quality with quantitative comparison to hand-traced centerlines as well as the results of leading commercial algorithms.

The generation of inbetween frames that interpolate a given set of key frames is a major component in the production of a 2D feature animation. Our objective is to considerably reduce the cost of the inbetweening phase by offering an intuitive and effective interactive environment that automates inbetweening when possible while allowing the artist to guide, complement, or override the results. Tight inbetweens, which interpolate similar key frames, are particularly time-consuming and tedious to draw. Therefore, we focus on automating these high-precision and expensive portions of the process. We have designed a set of user-guided semi-automatic techniques that fit well with current practice and minimize the number of required artist-gestures. We present a novel technique for stroke interpolation from only two keys which combines a stroke motion constructed from logarithmic spiral vertex trajectories with a stroke deformation based on curvature averaging and twisting warps. We discuss our system in the context of a feature animation production environment and evaluate our approach with real production data.

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We present Smart Scribbles-a new scribble-based interface for user-guided segmentation of digital sketchy drawings. In contrast to previous approaches based on simple selection strategies, Smart Scribbles exploits richer geometric and temporal information, resulting in a more intuitive segmentation interface. We introduce a novel energy minimization formulation in which both geometric and temporal information from digital input devices is used to define stroke-to-stroke and scribble-to-stroke relationships. Although the minimization of this energy is, in general, a NP-hard problem, we use a simple heuristic that leads to a good approximation and permits an interactive system able to produce accurate labelings even for cluttered sketchy drawings. We demonstrate the power of our technique in several practical scenarios such as sketch editing, as-rigid-as-possible deformation and registration, and on-the-fly labeling based on pre-classified guidelines.

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