Browsing by Author "Lau, Manfred"

Now showing 1 - 4 of 4
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    Font Specificity
    (The Eurographics Association, 2019) Power, Luther; Lau, Manfred; Cignoni, Paolo and Miguel, Eder
    We explore the concept of ''image specificity'' for fonts and introduce the notion of ''font specificity''. The idea is that a font that elicits consistent descriptions from different people are more ''specific''. We collect specificity-based data for fonts where participants are given each font and asked to describe it with words. We then analyze the data and characterize the qualitative features that make a font ''specific''. Finally, we show that the notion of font specificity can be learned and demonstrate some specificity-guided applications.
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    Learning 3D Shape Aesthetics Globally and Locally
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Chen, Minchan; Lau, Manfred; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    There exist previous works in computing the visual aesthetics of 3D shapes ''globally'', where the term global means that shape aesthetics data are collected for whole 3D shapes and then used to compute the aesthetics of whole 3D shapes. In this paper, we introduce a novel method that takes such ''global'' shape aesthetics data, and learn both a ''global'' shape aesthetics measure that computes aesthetics scores for whole 3D shapes, and a ''local'' shape aesthetics measure that computes to what extent a local region on the 3D shape surface contributes to the whole shape's aesthetics. These aesthetics measures are learned, and hence do not consider existing handcrafted notions of what makes a 3D shape aesthetic. We take a dataset of global pairwise shape aesthetics, where humans compares between pairs of shapes and say which shape from each pair is more aesthetic. Our solution proposes a point-based neural network that takes a 3D shape represented by surface patches as input and jointly outputs its global aesthetics score and a local aesthetics map. To build connections between global and local aesthetics, we embed the global and local features into the same latent space and then output scores with the weights-shared aesthetics predictors. Furthermore, we designed three loss functions to supervise the training jointly. We demonstrate the shape aesthetics results globally and locally to show that our framework can make good global aesthetics predictions while the predicted aesthetics maps are consistent with human perception. In addition, we present several applications enabled by our local aesthetics metric.
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    A Motion-guided Interface for Modeling 3D Multi-functional Furniture
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Chen, Minchan; Lau, Manfred; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
    While non-expert 3D design systems are helpful for performing conceptual design, most existing works focused on modeling static objects. However, the 3D modeling interfaces can include more interactions between the user and the models that are dynamic (and can be interacted with). In this paper, we propose a 3D modeling system for the conceptual design of interactable multi-functional furniture. Our contribution is in the design and development of a motion-guided interface. The key idea is that users should create interactable furniture components as if they are interacting with them with their hands. We conducted a preliminary user study to explore users' preferred hand gestures for creating various dynamic furniture components, implemented a 3D modeling system with the preferred gestures as a basis for the motion-guided user interface, and conducted an evaluation user study to demonstrate that our user interface is user-friendly and efficient for novice designers to perform conceptual furniture designs.
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    Schelling Meshes
    (The Eurographics Association, 2019) Power, Luther; Lau, Manfred; Cignoni, Paolo and Miguel, Eder
    The concept of ''Schelling points'' on 3D shapes has been explored for points on the surface of a 3D mesh. In this paper, we introduce the notion of ''Schelling meshes'' which extends the Schelling concept to 3D meshes as a whole themselves. We collect Schelling-based data for meshes where participants are given a group of shapes and asked to choose those with the aim of matching with what they expect others to choose. We analyze the data by computing the Schelling frequency of each shape and characterizing the qualitative features that make a shape ''Schelling''. We show that the Schelling frequencies can be learned and demonstrate Schelling-guided shape applications.