The miniaturization of shell structures presents a versatile and complex challenge, bridging geometry with diverse practical applications. In this paper, we introduce a novel approach for computing origami crease patterns to compress arbitrary 3D shell objects. First, we employ the adapted Material Point Method (MPM) to simulate the compression of a target surface and obtain an initial folded configuration. Since MPM produces overly smooth curved surfaces, their crease patterns are unsuitable for practical origami fabrication. We then propose a novel Folding Line Extraction (FLE) method that optimizes these smoothed surfaces to extract folding lines that achieve the target compression with minimal deformation and stretching outside the crease lines. This method produces smooth curved folding lines. Fabrication and experimental validation of the extracted patterns demonstrate their effectiveness and applicability in real-world scenarios.

Artist sketches often use multiple overdrawn strokes to depict a single intended curve. Humans effortlessly mentally consolidate such sketches by detecting groups of overdrawn strokes and replacing them with the corresponding intended curves. While this mental process is near instantaneous, manually annotating or retracing sketches to communicate this intended mental image is highly time consuming; yet most sketch applications are not designed to handle overdrawing and can only operate on overdrawing-free, consolidated sketches. We propose StripMaker , a new and robust learning based method for automatic consolidation of raw vector sketches. We avoid the need for an unsustainably large manually annotated learning corpus by leveraging observations about artist workflow and perceptual cues viewers employ when mentally consolidating sketches. We train two perception-aware classifiers that assess the likelihood that a pair of stroke groups jointly depicts the same intended curve: our first classifier is purely local and only accounts for the properties of the evaluated strokes; our second classifier incorporates global context and is designed to operate on approximately consolidated sketches. We embed these classifiers within a consolidation framework that leverages artist workflow: we first process strokes in the order they were drawn and use our local classifier to arrive at an approximate consolidation output, then use the contextual classifier to refine this output and finalize the consolidated result. We validate StripMaker by comparing its results to manual consolidation outputs and algorithmic alternatives. StripMaker achieves comparable performance to manual consolidation. In a comparative study participants preferred our results by a 53% margin over those of the closest algorithmic alternative (67% versus 14%, other/neither 19%).

Text chat applications are an integral part of daily social and professional communication. However, messages sent over text chat applications do not convey vocal or nonverbal information from the sender, and detecting the emotional tone in text-only messages is challenging. In this paper, we explore the effects of speech balloon shapes on the sender-receiver agreement regarding the emotionality of a text message. We first investigated the relationship between the shape of a speech balloon and the emotionality of speech text in Japanese manga. Based on these results, we created a system that automatically generates speech balloons matching linear emotional arousal intensity by Auxiliary Classifier Generative Adversarial Networks (ACGAN). Our evaluation results from a controlled experiment suggested that the use of emotional speech balloons outperforms the use of emoticons in decreasing the differences between message senders’ and receivers’ perceptions about the level of emotional arousal in text messages.