WYSIWYG Design of Hypnotic Line Art.

Hypnotic line art is a modern form in which white narrow curved ribbons, with the width and direction varying along each path over a black background, provide a keen sense of 3D objects regarding surface shapes and topological contours. However, the procedure of manually creating such line art work can be quite tedious and time-consuming. In this article, we present an interactive system that offers a What-You-See-Is-What-You-Get (WYSIWYG) scheme for producing hypnotic line art images by integrating and placing evenly-spaced streamlines in tensor fields. With an input picture segmented, the user just needs to sketch a few illustrative strokes to guide the construction of a tensor field for each part of the objects therein. Specifically, we propose a new method which controls, with great precision, the aesthetic layout and artistic drawing of an array of streamlines in each tensor field to emulate the style of hypnotic line art. Given several parameters for streamlines such as density, thickness, and sharpness, our system is capable of generating professional-level hypnotic line art work. With great ease of use, it allows art designers to explore a wide variety of possibilities to obtain hypnotic line art results of their own preferences.

High Relief from Brush Painting.

Relief is an art form part way between 3D sculpture and 2D painting. We present a novel approach for generating a texture-mapped high-relief model from a single brush painting. Our aim is to extract the brushstrokes from a painting and generate the individual corresponding relief proxies rather than recovering the exact depth map from the painting, which is a tricky computer vision problem, requiring assumptions that are rarely satisfied. The relief proxies of brushstrokes are then combined together to form a 2.5D high-relief model. To extract brushstrokes from 2D paintings, we apply layer decomposition and stroke segmentation by imposing boundary constraints. The segmented brushstrokes preserve the style of the input painting. By inflation and a displacement map of each brushstroke, the features of brushstrokes are preserved by the resultant high-relief model of the painting. We demonstrate that our approach is able to produce convincing high-reliefs from a variety of paintings(with humans, animals, flowers, etc.). As a secondary application, we show how our brushstroke extraction algorithm could be used for image editing. As a result, our brushstroke extraction algorithm is specifically geared towards paintings with each brushstroke drawn very purposefully, such as Chinese paintings, Rosemailing paintings, etc.

Interactive High-Relief Reconstruction for Organic and Double-Sided Objects from a Photo.

We introduce an interactive user-driven method to reconstruct high-relief 3D geometry from a single photo. Particularly, we consider two novel but challenging reconstruction issues: i) common non-rigid objects whose shapes are organic rather than polyhedral/symmetric, and ii) double-sided structures, where front and back sides of some curvy object parts are revealed simultaneously on image. To address these issues, we develop a three-stage computational pipeline. First, we construct a 2.5D model from the input image by user-driven segmentation, automatic layering, and region completion, handling three common types of occlusion. Second, users can interactively mark-up slope and curvature cues on the image to guide our constrained optimization model to inflate and lift up the image layers. We provide real-time preview of the inflated geometry to allow interactive editing. Third, we stitch and optimize the inflated layers to produce a high-relief 3D model. Compared to previous work, we can generate high-relief geometry with large viewing angles, handle complex organic objects with multiple occluded regions and varying shape profiles, and reconstruct objects with double-sided structures. Lastly, we demonstrate the applicability of our method on a wide variety of input images with human, animals, flowers, etc.

A unified approach for revealing multiple balance recovery strategies.

In human balance recovery, different strategies have been proposed with generally overlooked knee motions but extensive focus on the ankle, hip, and step strategies. It is not well understood whether maintaining balance is regulated at the lower "muscular-articular" level of coordinating segment joints or at a higher level of controlling whole body dynamics. Whether balance control is to minimize joint degrees of freedom (DOF) or utilize all the available DOF also remains unclear. This study aimed to use a realistic musculoskeletal human model to identify multiple balance recovery strategies with a single optimization criterion. Movements were driven by neural excitations (which activated muscle force generation) and were assumed to be symmetric. Balance recoveries were simulated with forward-inclined straight body postures as the initial conditions. When the position of the toes was fixed, balance was regained with virtually straight knees and mixed ankle/hip strategies. Under a severely perturbed condition, use of the forward hop strategy after releasing the fixed-toes constraint indicated spontaneous recruitment or suppression of DOF, which mimicked functions of optimally computed CNS commands in humans. The results also indicated that increase/decrease in the number of DOF depends on the imposed perturbation intensity and movement constraints.

2.5D Cartoon Hair Modeling and Manipulation.

This paper addresses a challenging single-view modeling and animation problem with cartoon images. Our goal is to model the hairs in a given cartoon image with consistent layering and occlusion, so that we can produce various visual effects from just a single image. We propose a novel 2.5D modeling approach to deal with this problem. Given an input image, we first segment the hairs of the cartoon character into regions of hair strands. Then, we apply our novel layering metric, which is derived from the Gestalt psychology, to automatically optimize the depth ordering among the hair strands. After that, we employ our hair completion method to fill the occluded part of each hair strand, and create a 2.5D model of the cartoon hair. By using this model, we can produce various visual effects, e.g., we develop a simplified fluid simulation model to produce wind blowing animations with the 2.5D hairs. To further demonstrate the applicability and versatility of our method, we compare our results with real cartoon hair animations, and also apply our model to produce a wide variety of hair manipulation effects, including hair editing and hair braiding.

Double-Sided 2.5D Graphics.

This paper introduces double-sided 2.5D graphics, aiming at enriching the visual appearance when manipulating conventional 2D graphical objects in 2.5D worlds. By attaching a back texture image on a single-sided 2D graphical object, we can enrich the surface and texture detail on 2D graphical objects and improve our visual experience when manipulating and animating them. A family of novel operations on 2.5D graphics, including rolling, twisting, and folding, are proposed in this work, allowing users to efficiently create compelling 2.5D visual effects. Very little effort is needed from the user's side. In our experiment, various creative designs on double-sided graphics were worked out by the recruited participants including a professional artist, which show and demonstrate the feasibility and applicability of our proposed method.

Content-aware video retargeting using object-preserving warping.

A novel content-aware warping approach is introduced for video retargeting. The key to this technique is adapting videos to fit displays with various aspect ratios and sizes while preserving both visually salient content and temporal coherence. Most previous studies solve this spatiotemporal problem by consistently resizing content in frames. This strategy significantly improves the retargeting results, but does not fully consider object preservation, sometimes causing apparent distortions on visually salient objects. We propose an object-preserving warping scheme with object-based significance estimation to reduce this unpleasant distortion. In the proposed scheme, visually salient objects in 3D space-time space are forced to undergo as-rigid-as-possible warping, while low-significance contents are warped as close as possible to linear rescaling. These strategies enable our method to consistently preserve both the spatial shapes and temporal motions of visually salient objects and avoid overdeformations on low-significance objects, yielding a pleasing motion-aware video retargeting. Qualitative and quantitative analyses, including a user study and experiments on complex videos containing diverse cameras and dynamic motions, show a clear superiority of our method over related video retargeting methods.