Browsing by Author "Comino Trinidad, Marc"

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    PERGAMO: Personalized 3D Garments from Monocular Video
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Casado-Elvira, Andrés; Comino Trinidad, Marc; Casas, Dan; Dominik L. Michels; Soeren Pirk
    Clothing plays a fundamental role in digital humans. Current approaches to animate 3D garments are mostly based on realistic physics simulation, however, they typically suffer from two main issues: high computational run-time cost, which hinders their deployment; and simulation-to-real gap, which impedes the synthesis of specific real-world cloth samples. To circumvent both issues we propose PERGAMO, a data-driven approach to learn a deformable model for 3D garments from monocular images. To this end, we first introduce a novel method to reconstruct the 3D geometry of garments from a single image, and use it to build a dataset of clothing from monocular videos. We use these 3D reconstructions to train a regression model that accurately predicts how the garment deforms as a function of the underlying body pose. We show that our method is capable of producing garment animations that match the real-world behavior, and generalizes to unseen body motions extracted from motion capture dataset.
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    Sensor-aware Normal Estimation for Point Clouds from 3D Range Scans
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Comino Trinidad, Marc; Andujar, Carlos; Chica, Antonio; Brunet, Pere; Ju, Tao and Vaxman, Amir
    Normal vectors are essential for many point cloud operations, including segmentation, reconstruction and rendering. The robust estimation of normal vectors from 3D range scans is a challenging task due to undersampling and noise, specially when combining points sampled from multiple sensor locations. Our error model assumes a Gaussian distribution of the range error with spatially-varying variances that depend on sensor distance and reflected intensity, mimicking the features of Lidar equipment. In this paper we study the impact of measurement errors on the covariance matrices of point neighborhoods. We show that covariance matrices of the true surface points can be estimated from those of the acquired points plus sensordependent directional terms. We derive a lower bound on the neighbourhood size to guarantee that estimated matrix coefficients will be within a predefined error with a prescribed probability. This bound is key for achieving an optimal trade-off between smoothness and fine detail preservation. We also propose and compare different strategies for handling neighborhoods with samples coming from multiple materials and sensors. We show analytically that our method provides better normal estimates than competing approaches in noise conditions similar to those found in Lidar equipment.