3D Custom Fit Garment Design with Body Movement

Wolff, Katja; Herholz, Philipp; Ziegler, Verena; Link, Frauke; Brügel, Nico; Sorkine‐Hornung, Olga

doi:10.48550/arxiv.2102.05462

Cited by 3 publications

(6 citation statements)

References 35 publications

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“…The work produces plausible sewing patterns for various garment types but lacks in pattern quality and relies on the quality of original geometry a lot. Another recent work [Wolff et al 2021] relies on having direct access to the garment rest shape, which allows producing working patterns for arbitrary garment designs with a general technique of Variational Surface Cutting. On the other hand, our learning-based approach neither requires uncorrupted, unsimulated geometry as input, nor additional pre-processing, and is capable of producing clean panel shapes.…”

Section: Garment Rest Shape Estimationmentioning

confidence: 99%

NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments

Korosteleva,

Lee

2022

Preprint

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Section: Garment Rest Shape Estimationmentioning

confidence: 99%

NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments

Korosteleva,

Lee

2022

Preprint

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“…10. Several frames of a cloth simulation using the method of Baraff and Witkin [1998] as implemented by Wolff et al [2021].…”

Section: As-rigid-as-possible Surface Modelingmentioning

confidence: 99%

“…To demonstrate the practicality of our system for more complex computations, we generate code for assembling the system matrix and the right-hand side. To this end we run a templated version of an implementation provided by Wolff et al [2021]. The final system has the form…”

Section: Cloth Simulationmentioning

confidence: 99%

Sparsity-Specific Code Optimization using Expression Trees

Herholz¹,

Tang²,

Schneider³

et al. 2021

Preprint

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We introduce a code generator that converts unoptimized C++ code operating on sparse data into vectorized and parallel CPU or GPU kernels. Our approach unrolls the computation into a massive expression graph, performs redundant expression elimination, grouping, and then generates an architecture-specific kernel to solve the same problem, assuming that the sparsity pattern is fixed, which is a common scenario in many applications in computer graphics and scientific computing. We show that our approach scales to large problems and can achieve speedups of two orders of magnitude on CPUs and three orders of magnitude on GPUs, compared to a set of manually optimized CPU baselines. To demonstrate the practical applicability of our approach, we employ it to optimize popular algorithms with applications to physical simulation and interactive mesh deformation.

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“…There are many powerful techniques to model shapes directly in 3D, with various approaches specifically developed for 3D garment modeling, e.g. with sketch based user interfaces Decaudin et al 2006;Robson et al 2011;Rose et al 2007;Wolff et al 2021], input gestures in the physical [Wibowo et al 2012] or virtual reality [TiltBrush 2022], via 3D scanning [Pons-Moll et al 2017] or data driven modeling . Advances in human body modeling [Alldieck et al 2021;Bogo et al 2014;Loper et al 2015;Osman et al 2020] facilitate custom digital tailoring of garments that fit the personalized body avatar without having to rely on standard sizes.…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art approaches are able to compute optimal cuts that balance the flattening distortion and cut length [Li et al 2018a;Sharp and Crane 2018]. Recent work [Wolff et al 2021] applies such a technique to create the sewing pattern, but the resulting panel shapes are visually far from common practice in fashion and challenging to sew. The problem is that most segmentation techniques are general and do not account for the specific setting of cloth pattern making and the fabrication constraints unique to it.…”

Section: Introductionmentioning

confidence: 99%

Computational pattern making from 3D garment models

et al. 2022

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We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach.

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3D Custom Fit Garment Design with Body Movement

Abstract: be used to parameterize it into a 2D sewing pattern or transform it into a knitting pattern.

Cited by 3 publications

References 35 publications

NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments

NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments

Sparsity-Specific Code Optimization using Expression Trees

Computational pattern making from 3D garment models

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