論文アブストラクト： The mission of animators is to create nuanced, high-quality character motions. To achieve this, the careful editing of animation curves---curves that determine how a series of keyframed poses are interpolated over time---is an important task. Manual editing affords full and precise control, but requires tedious and nonintuitive trials and errors. Numerical optimization can automate such exploration; however, automatic solutions cannot always be perfect, and it is difficult for animators to control optimization owing to its black-box behavior. In this paper, we present a new framework called optimization-guided motion editing, which is aimed at maintaining a sense of full control while utilizing the power of optimization. We have designed interactions and developed a set of mathematical formulations to enable them. We discuss the framework's potential by demonstrating several usage scenarios with our proof-of-concept system, named OptiMo.
論文アブストラクト： Hand motion and pen drawing can be intuitive and expressive inputs for professional digital 3D authoring. However, their inherent limitations have hampered wider adoption. 3D sketching using hand motion is rapid but rough, and 3D sketching using pen drawing is delicate but tedious. Our new 3D sketching workflow combines these two in a complementary manner. The user makes quick hand motions in the air to generate approximate 3D shapes, and uses them as scaffolds on which to add details via pen-based 3D sketching on a tablet device. Our air scaffolding technique and corresponding algorithm extract only the intended shapes from unconstrained hand motions. Then, the user sketches 3D ideas by defining sketching planes on these scaffolds while appending new scaffolds, as needed. A user study shows that our progressive and iterative workflow enables more agile 3D sketching compared to ones using either hand motion or pen drawing alone.
Hand motionとPen drawingという2つの3Dスケッチ手法の良いとこ取りをした3Dスケッチワークフローの提案。Hand motionでの下書き、Pen drawingでの仕上げにより、素早く精密に3Dスケッチを描くことができる。
論文アブストラクト： We present Qetch, a tool where users freely sketch patterns on a scale-less canvas to query time series data without specifying query length or amplitude. We study how humans sketch time series patterns --- humans preserve visually salient perceptual features but often non-uniformly scale and locally distort a pattern --- and we develop a novel matching algorithm that accounts for human sketching errors. Qetch enables the easy construction of complex and expressive queries with two key features: regular expressions over sketches and relative positioning of sketches to query multiple time-aligned series. Through user studies, we demonstrate the effectiveness of Qetch's different interaction features. We also demonstrate the effectiveness of Qetch's matching algorithm compared to popular algorithms on targeted, and exploratory query-by-sketch search tasks on a variety of data sets.
論文アブストラクト： Creating sketch animations using traditional tools requires special artistic skills, and is tedious even for trained professionals. To lower the barrier for creating sketch animations, we propose a new system, emphLive Sketch,</i> which allows novice users to interactively bring static drawings to life by applying deformation-based animation effects that are extracted from video examples. Dynamic deformation is first extracted as a sparse set of moving control points from videos and then transferred to a static drawing. Our system addresses a few major technical challenges, such as motion extraction from video, video-to-sketch alignment, and many-to-one motion-driven sketch animation. While each of the sub-problems could be difficult to solve fully automatically, we present reliable solutions by combining new computational algorithms with intuitive user interactions. Our pilot study shows that our system allows both users with or without animation skills to easily add dynamic deformation to static drawings.