Browsing by Author "Ceylan, Duygu"

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    Deep Detail Enhancement for Any Garment
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhang, Meng; Wang, Tuanfeng; Ceylan, Duygu; Mitra, Niloy J.; Mitra, Niloy and Viola, Ivan
    Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low-resolution physically-based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data-driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch-based formulation, that produces high-resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light-weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion. Project page: http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/
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    EUROGRAPHICS 2025: Short Papers Frontmatter
    (Eurographics Association, 2025) Ceylan, Duygu; Li, Tzu-Mao; Ceylan, Duygu; Li, Tzu-Mao
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    Neural Garment Dynamics via Manifold-Aware Transformers
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Peizhuo; Wang, Tuanfeng Y.; Kesdogan, Timur Levent; Ceylan, Duygu; Sorkine-Hornung, Olga; Bermano, Amit H.; Kalogerakis, Evangelos
    Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect to a fixed parametric human body model and are limited to garment geometries that were seen during training. In this work, we take a different approach and model the dynamics of a garment by exploiting its local interactions with the underlying human body. Specifically, as the body moves, we detect local garment-body collisions, which drive the deformation of the garment. At the core of our approach is a mesh-agnostic garment representation and a manifold-aware transformer network design, which together enable our method to generalize to unseen garment and body geometries. We evaluate our approach on a wide variety of garment types and motion sequences and provide competitive qualitative and quantitative results with respect to the state of the art.