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Has files: true × Subject: Computing methodologies → Shape modeling ×

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    Driller: An Intuitive Interface for Designing Tangled and Nested Shapes
    (The Eurographics Association, 2024) Butler, Tara; Guehl, Pascal; Parakkat, Amal Dev; Cani, Marie-Paule; Hu, Ruizhen; Charalambous, Panayiotis
    The ability to represent not only isolated shapes but also shapes that interact is essential in various fields, from design to biology or anatomy. In this paper, we propose an intuitive interface to control and edit complex shape arrangements. Using a set of pre-defined shapes that may intersect, our ''Driller'' interface allows users to trigger their local deformation so that they rest on each other, become tangled, or even nest within each other. Driller provides an intuitive way to specify the relative depth of different shapes beneath user-selected points of interest by setting their local depth ordering perpendicularly to the camera's viewpoint. Deformations are then automatically generated by locally propagating these ordering constraints. In addition to being part of the final arrangement, some of the shapes can be used as deformers, which can be later deleted to help sculpt the target shapes. We implemented this solution within a sketch-based modeling system designed for novice users.
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    SPnet: Estimating Garment Sewing Patterns from a Single Image of a Posed User
    (The Eurographics Association, 2024) Lim, Seungchan; Kim, Sumin; Lee, Sung-Hee; Hu, Ruizhen; Charalambous, Panayiotis
    This paper presents a novel method for reconstructing 3D garment models from a single image of a posed user. Previous studies that have primarily focused on accurately reconstructing garment geometries to match the input garment image may often result in unnatural-looking garments when deformed for new poses. To overcome this limitation, our work takes a different approach by inferring the fundamental shape of the garment through sewing patterns from a single image, rather than directly reconstructing 3D garments. Our method consists of two stages. Firstly, given a single image of a posed user, it predicts the garment image worn on a T-pose, representing the baseline form of the garment. Then, it estimates the sewing pattern parameters based on the T-pose garment image. By simulating the stitching and draping of the sewing pattern using physics simulation, we can generate 3D garments that can adaptively deform to arbitrary poses. The effectiveness of our method is validated through ablation studies on the major components and a comparison with other methods.