Sketch2Stress: Sketching With Structural Stress Awareness.

In the process of product design and digital fabrication, the structural analysis of a designed prototype is a fundamental and essential step. However, such a step is usually invisible or inaccessible to designers at the early sketching phase. This limits the user's ability to consider a shape's physical properties and structural soundness. To bridge this gap, we introduce a novel approach Sketch2Stress that allows users to perform structural analysis of desired objects at the sketching stage. This method takes as input a 2D freehand sketch and one or multiple locations of user-assigned external forces. With the specially-designed two-branch generative-adversarial framework, it automatically predicts a normal map and a corresponding structural stress map distributed over the user-sketched underlying object. In this way, our method empowers designers to easily examine the stress sustained everywhere and identify potential problematic regions of their sketched object. Furthermore, combined with the predicted normal map, users are able to conduct a region- wise structural analysis efficiently by aggregating the stress effects of multiple forces in the same direction. Finally, we demonstrate the effectiveness and practicality of our system with extensive experiments and user studies.

Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects.

We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and structurally. Beautifying a sketch is challenging because of its highly abstract and heavily diverse drawing manner. Existing methods are usually confined to their limited training samples and thus cannot beautify freely drawn sketches with both geometric and structural variations. To address this challenge, we adopt a divide-and-combine strategy. Specifically, we first parse an input sketch into semantic components, beautify individual components by a learned part beautification module based on part-level implicit manifolds, and then reassemble the beautified components through a structure beautification module. With this strategy, our method can go beyond the training samples and handle novel freehand sketches. We demonstrate the effectiveness of our system with extensive experiments and a perceptual study.

Comparing Shape Representations for the Aesthetic Judgment of 3-D Shape Pairs.

Visual aesthetics of 3-D shapes is a fundamental perceptual attribute. In this article, we explore the question of how different shape representations affect the aesthetic judgments of shape pairs. Specifically, we compare human responses to perceptual aesthetics judgments on 3-D shapes in pairs presented in different 3-D shape representation, such as voxels, points, wireframe, and polygon. In contrast to our own previous work [Dev et al. (2017)] that explores this question for a few shape classes, this article analyzes a larger number of shape classes. Our key finding is that human aesthetics judgments on relatively low resolution of points or voxels are comparable to polygon meshes, which implies that humans can often make their aesthetics decisions based on relatively coarse representations of the shapes. Our results have implications toward the data collection process of pairwise aesthetics data and the further use of such data in shape aesthetics and 3-D modeling problems.

Learning Perceptual Aesthetics of 3-D Shapes From Multiple Views.

The quantification of 3-D shape aesthetics has so far focused on specific shape features and manually defined criteria such as the curvature and the rule of thirds. In this article, we built a model of 3-D shape aesthetics directly from human aesthetics preference data and show it to be well aligned with human perception of aesthetics. To build this model, we first crowdsource a large number of human aesthetics preferences by showing shapes in pairs in an online study and then use the same to build a 3-D shape multiview-based deep neural network architecture to allow us to learn a measure of 3-D shape aesthetics. In comparison to previous approaches, we do not use any predefined notions of aesthetics to build our model. Our algorithmically computed measure of shape aesthetics is beneficial to a range of applications in graphics such as search, visualization, and scene composition.

Creating universal health care in Ireland: A legal context.

In this paper we establish a working definition of, and develop a legal rationale for, the insertion of a constitutional Right to Health (RTH) protection in the Constitution of Ireland. We propose that a legal framework exists for the judicial enforcement of a right to health in Ireland, as based on parallels drawn between Irish case law and that of RSA, a comparable common law constitutional democracy with a developed jurisprudential approach to its constitutional RTH. When modelled after precedential international provisions, this right strengthens and defends health policy goals (such as universal health care) through a common-law system of governmental accountability. Additionally, national rights to health have observable correlations with improved public health, and it stimulates institutional initiatives. The 1937 Constitution of Ireland includes several personal, social, and economic rights, and a RTH would complement the existing right to primary education as a socio-economic right. We note these considerations were discussed during the legislative proposal made in the 32nd Dáil for a constitutional RTH, which emerged in response to Ireland's ongoing efforts toward health policy reform.