Olga Sorkine-Hornung

Last Name

Sorkine-Hornung

First Name

Olga

ORCID

0000-0002-8089-3974

Organisational unit

03911 - Sorkine Hornung, Olga / Sorkine Hornung, Olga

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Publications 1 - 10 of 151

Dev2PQ: Planar Quadrilateral Strip Remeshing of Developable Surfaces
Verhoeven, Floor; Vaxman, Amir; Hoffmann, Tim; et al. (2022)
ACM Transactions on Graphics
We introduce an algorithm to remesh triangle meshes representing developable surfaces to planar quad dominant meshes. The output of our algorithm consists of planar quadrilateral (PQ) strips that are aligned to principal curvature directions and closely approximate the curved parts of the input developable, and planar polygons representing the flat parts of the input that connect the PQ strips. Developable PQ-strip meshes are useful in many areas of shape modeling, thanks to the simplicity of fabrication from flat sheet material. Unfortunately, they are difficult to model due to their restrictive combinatorics. Other representations of developable surfaces, such as arbitrary triangle or quad meshes, are more suitable for interactive freeform modeling but generally have non-planar faces or are not aligned to principal curvatures. Our method leverages the modeling flexibility of non-ruling-based representations of developable surfaces while still obtaining developable, curvature-aligned PQ-strip meshes. Our algorithm optimizes for a scalar function on the input mesh, such that its isolines are extrinsically straight and align well to the locally estimated ruling directions. The condition that guarantees straight isolines is non-linear of high order and numerically difficult to enforce in a straightforward manner. We devise an alternating optimization method that makes our problem tractable and practical to compute. Our method works automatically on any developable input, including multiple patches and curved folds, without explicit domain decomposition. We demonstrate the effectiveness of our approach on a variety of developable surfaces and show how our remeshing can be used alongside handle-based interactive freeform modeling of developable shapes.
GarmentCode: Programming Parametric Sewing Patterns
Korosteleva, Maria; Sorkine-Hornung, Olga (2023)
ACM Transactions on Graphics
Garment modeling is an essential task of the global apparel industry and a core part of digital human modeling. Realistic representation of garments with valid sewing patterns is key to their accurate digital simulation and eventual fabrication. However, little-to-no computational tools provide support for bridging the gap between high-level construction goals and low-level editing of pattern geometry, e.g., combining or switching garment elements, semantic editing, or design exploration that maintains the validity of a sewing pattern. We suggest the first DSL for garment modeling - GarmentCode - that applies principles of object-oriented programming to garment construction and allows designing sewing patterns in a hierarchical, component-oriented manner. The programming-based paradigm naturally provides unique advantages of component abstraction, algorithmic manipulation, and free-form design parametrization. We additionally support the construction process by automating typical low-level tasks like placing a dart at a desired location. In our prototype garment configurator, users can manipulate meaningful design parameters and body measurements, while the construction of pattern geometry is handled by garment programs implemented with GarmentCode. Our configurator enables the free exploration of rich design spaces and the creation of garments using interchangeable, parameterized components. We showcase our approach by producing a variety of garment designs and retargeting them to different body shapes using our configurator. The library and garment configurator are available at https://github.com/maria-korosteleva/GarmentCode.
AIpparel: A Multimodal Foundation Model for Digital Garments
Nakayama, Kiyohiro; Ackermann, Jan; Kesdogan, Timur; et al. (2025)
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Apparel is essential to human life, offering protection, mirroring cultural identities, and showcasing personal style. Yet, the creation of garments remains a time-consuming process, largely due to the manual work involved in designing them. To simplify this process, we introduce AIpparel, a multimodal foundation model for generating and editing sewing patterns. Our model fine-tunes state-of-the-art large multimodal models (LMMs) on a custom-curated large-scale dataset of over 120,000 unique garments, each with multimodal annotations including text, images, and sewing patterns. Additionally, we propose a novel tokenization scheme that concisely encodes these complex sewing patterns so that LLMs can learn to predict them efficiently. AIpparel achieves state-of-the-art performance in single-modal tasks, including text-to-garment and image-to-garment prediction, and enables novel multimodal garment generation applications such as interactive garment editing. The project website is at https: //georgenakayama.github.io/AIpparel/.
Facial Performance Enhancement Using Dynamic Shape Space Analysis
Bermano, Amit H.; Bradley, Derek; Beeler, Thabo; et al. (2014)
ACM Transactions on Graphics
Robust Image Retargeting via Axis-Aligned Deformation
Panozzo, Daniele; Weber, Ofir; Sorkine-Hornung, Olga (2012)
Computer Graphics Forum
Cusps of Characteristic Curves and Intersection-Aware Visualization of Path and Streak Lines
Weinkauf, Tino; Theisel, Holger; Sorkine-Hornung, Olga (2011)
Mathematics and Visualization ~ Topological Methods in Data Analysis and Visualization II : Theory, Algorithms, and Applications
Ink-and-Ray
Sykora, Daniel; Kavan, Ladislav; Cadik, Martin; et al. (2014)
ACM Transactions on Graphics
Deformation capture via soft and stretchable sensor arrays
Glauser, Oliver; Panozzo, Daniele; Hilliges, Otmar; et al. (2019)
ACM Transactions on Graphics
We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line-of-sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive layers with complex geometries into silicone compounds. Overlapping conductive areas from separate layers form local capacitors that measure dense area changes. Contrary to existing fabrication methods, the proposed technique only requires hardware that is readily available in modern fablabs. While area measurements alone are not enough to reconstruct the full 3D deformation of a surface, they become sufficient when paired with a data-driven prior. A novel semi-automatic tracking algorithm, based on an elastic surface geometry deformation, allows us to capture ground-truth data with an optical mocap system, even under heavy occlusions or partially unobservable markers. The resulting dataset is used to train a regressor based on deep neural networks, directly mapping the area readings to global positions of surface vertices. We demonstrate the flexibility and accuracy of the proposed hardware and software in a series of controlled experiments and design a prototype of wearable wrist, elbow, and biceps sensors, which do not require line-of-sight and can be worn below regular clothing.
Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Zhao, Qingqing; Li, Peizhuo; Wang, Yifan; et al. (2024)
Computer Graphics Forum
Creating plausible motions for a diverse range of characters is a long-standing goal in computer graphics. Current learning-based motion synthesis methods rely on large-scale motion datasets, which are often difficult if not impossible to acquire. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extracted from images using recent advancements in computer vision. In this paper, we tap into this alternative data source and introduce a neural motion synthesis approach through retargeting, which generates plausible motion of various characters that only have pose data by transferring motion from one single existing motion capture dataset of another drastically different characters. Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images. Additionally, a conducted user study indicated that a majority of participants found our retargeted motion to be more enjoyable to watch, more lifelike in appearance, and exhibiting fewer artifacts. Our code and dataset can be accessed here.
TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization
Binninger, Alexandre; Wiersma, Ruben; Herholz, Philipp; et al. (2025)
ACM Transactions on Graphics
We introduce TetWeave, a novel isosurface representation for gradient-based mesh optimization that jointly optimizes the placement of a tetrahedral grid used for Marching Tetrahedra and a novel directional signed distance at each point. TetWeave constructs tetrahedral grids on-the-fly via Delaunay triangulation, enabling increased flexibility compared to predefined grids. The extracted meshes are guaranteed to be watertight, two-manifold and intersection-free. The flexibility of TetWeave enables a resampling strategy that places new points where reconstruction error is high and allows to encourage mesh fairness without compromising on reconstruction error. This leads to high-quality, adaptive meshes that require minimal memory usage and few parameters to optimize. Consequently, TetWeave exhibits near-linear memory scaling relative to the vertex count of the output mesh — a substantial improvement over predefined grids. We demonstrate the applicability of TetWeave to a broad range of challenging tasks in computer graphics and vision, such as multi-view 3D reconstruction, mesh compression and geometric texture generation. Our code is available at https://github.com/AlexandreBinninger/TetWeave.

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Olga Sorkine-Hornung

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