DSpace Repository :: Browsing by Author "Marin, Riccardo"

Browsing by Author "Marin, Riccardo"

Now showing 1 - 9 of 9

CMH: Coordinates Manifold Harmonics for Functional Remeshing
(The Eurographics Association, 2019) Marin, Riccardo; Melzi, Simone; Musoni, Pietro; Bardon, Filippo; Tarini, Marco; Castellani, Umberto; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
In digital world reconstruction, 2-dimensional surface of real objects are often obtained as polygonal meshes after an acquisition procedure using 3D sensors. However, such representation requires several manual efforts from highly experts to correct the irregularity of tessellation and make it suitable for professional applications, such as those in the gaming or movie industry. Moreover, for modelling and animation purposes it is often required that the same connectivity is shared among two or more different shapes. In this paper we propose a new method that exploits a remeshing-by-matching approach where the observed noisy shape inherits a regular tessellation from a target shape which already satisfies the professional constraints. A fully automatic pipeline is introduced based on a variation of the functional mapping framework. In particular, a new set of basis functions, namely the Coordinates Manifold Harmonics (CMH), is properly designed for this tessellation transfer task. In our experiments an exhaustive quantitative and quality evaluation is reported for human body shapes in T-pose where the effectiveness of the proposed functional remeshing is clearly shown in comparison with other methods.
A functional skeleton transfer
(ACM, 2021) Musoni, Pietro; Marin, Riccardo; Melzi, Simone; Castellani, Umberto; Narain, Rahul and Neff, Michael and Zordan, Victor
The animation community has spent significant effort trying to ease rigging procedures. This is necessitated because the increasing availability of 3D data makes manual rigging infeasible. However, object animations involve understanding elaborate geometry and dynamics, and such knowledge is hard to infuse even with modern data-driven techniques. Automatic rigging methods do not provide adequate control and cannot generalize in the presence of unseen artifacts. As an alternative, one can design a system for one shape and then transfer it to other objects. In previous work, this has been implemented by solving the dense point-to-point correspondence problem. Such an approach requires a significant amount of supervision, often placing hundreds of landmarks by hand. This paper proposes a functional approach for skeleton transfer that uses limited information and does not require a complete match between the geometries. To do so, we suggest a novel representation for the skeleton properties, namely the functional regressor, which is compact and invariant to different discretizations and poses. We consider our functional regressor a new operator to adopt in intrinsic geometry pipelines for encoding the pose information, paving the way for several new applications. We numerically stress our method on a large set of different shapes and object classes, providing qualitative and numerical evaluations of precision and computational efficiency. Finally, we show a preliminar transfer of the complete rigging scheme, introducing a promising direction for future explorations.
Inverse Computational Spectral Geometry
(The Eurographics Association, 2022) Rodolà, Emanuele; Cosmo, Luca; Ovsjanikov, Maks; Rampini, Arianna; Melzi, Simone; Bronstein, Michael; Marin, Riccardo; Hahmann, Stefanie; Patow, Gustavo A.
In the last decades, geometry processing has attracted a growing interest thanks to the wide availability of new devices and software that make 3D digital data available and manipulable to everyone. Typical issues faced by geometry processing algorithms include the variety of discrete representations for 3D data (point clouds, polygonal or tet-meshes and voxels), or the type of deformation this data may undergo. Powerful approaches to address these issues come from looking at the spectral decomposition of canonical differential operators, such as the Laplacian, which provides a rich, informative, robust, and invariant representation of the 3D objects. The focus of this tutorial is on computational spectral geometry. We will offer a different perspective on spectral geometric techniques, supported by recent successful methods in the graphics and 3D vision communities and older but notoriously overlooked results. We will discuss both the “forward” path typical of spectral geometry pipelines (e.g. computing Laplacian eigenvalues and eigenvectors of a given shape) with its widespread applicative relevance, and the inverse path (e.g. recovering a shape from given Laplacian eigenvalues, like in the classical “hearing the shape of the drum” problem) with its ill-posed nature and the benefits showcased on several challenging tasks in graphics and geometry processing.
Localized Shape Modelling with Global Coherence: An Inverse Spectral Approach
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Pegoraro, Marco; Melzi, Simone; Castellani, Umberto; Marin, Riccardo; Rodolà, Emanuele; Campen, Marcel; Spagnuolo, Michela
Many natural shapes have most of their characterizing features concentrated over a few regions in space. For example, humans and animals have distinctive head shapes, while inorganic objects like chairs and airplanes are made of well-localized functional parts with specific geometric features. Often, these features are strongly correlated - a modification of facial traits in a quadruped should induce changes to the body structure. However, in shape modelling applications, these types of edits are among the hardest ones; they require high precision, but also a global awareness of the entire shape. Even in the deep learning era, obtaining manipulable representations that satisfy such requirements is an open problem posing significant constraints. In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand. Modifications to these spectra are translated to semantically valid deformations of the corresponding surface. By explicitly decoupling the global from the local surface features, our pipeline allows to perform local edits while simultaneously maintaining a global stylistic coherence. We empirically demonstrate how our learning-based model generalizes to shape representations not seen at training time, and we systematically analyze different choices of local operators over diverse shape categories.
Merging, extending and learning representations for 3D shape matching
(2021-05-15) Marin, Riccardo
In the last decades, researchers devoted considerable attention to shape matching. Correlating surfaces unlocks otherwise impossible applications and analysis. However, non-rigid objects (like humans) have an enormous range of possibilities to deform their surfaces, making the correspondence challenging to obtain. Computer Graphics and Vision has developed many different representations, each with its peculiarities, conveying different properties and easing different tasks. In this thesis, we exploit, extend, and propose representations to establish correspondences in the non-rigid domain. First, we show how the latent representation of a morphable model can be combined with the spectral embedding, acting as regularization of registration pipelines. We fill the gap in unconstrained problems like occlusion in RGB+D single view or partiality and topological noise for 3D representations. Furthermore, we define a strategy to densify the morphable model discretization and catch variable quantities of details. We also analyze how different discretizations impact correspondence computation. Therefore, we combine intrinsic and extrinsic embeddings, obtaining a robust representation that lets us transfer triangulation among the shapes. Data-driven techniques are particularly relevant to catch complex priors. Hence, we use deep learning techniques to obtain a new high-dimensional embedding for point clouds; in this representation, the objects align with a linear transformation. This approach shows resilience to sparsity and noise. Finally, we connect super-compact latent representations by linking autoencoder latent codes with Laplace-Beltrami operator spectra. This strategy lets us solving a complicated historical problem, enriching the learning framework with geometric properties, and matching objects regardless of their representations. The main contributions of this thesis are the theoretical and practical studies of representations, the advancement in shape matching, and finally, the data and code produced and publicly available.
MoMaS: Mold Manifold Simulation for Real-time Procedural Texturing
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Maggioli, Filippo; Marin, Riccardo; Melzi, Simone; Rodolà, Emanuele; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
The slime mold algorithm has recently been under the spotlight thanks to its compelling properties studied across many disciplines like biology, computation theory, and artificial intelligence. However, existing implementations act only on planar surfaces, and no adaptation to arbitrary surfaces is available. Inspired by this gap, we propose a novel characterization of the mold algorithm to work on arbitrary curved surfaces. Our algorithm is easily parallelizable on GPUs and allows to model the evolution of millions of agents in real-time over surface meshes with several thousand triangles, while keeping the simplicity proper of the slime paradigm. We perform a comprehensive set of experiments, providing insights on stability, behavior, and sensibility to various design choices. We characterize a broad collection of behaviors with a limited set of controllable and interpretable parameters, enabling a novel family of heterogeneous and high-quality procedural textures. The appearance and complexity of these patterns are well-suited to diverse materials and scopes, and we add another layer of generalization by allowing different mold species to compete and interact in parallel.
POP: Full Parametric model Estimation for Occluded People
(The Eurographics Association, 2019) Marin, Riccardo; Melzi, Simone; Mitra, Niloy J.; Castellani, Umberto; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
In the last decades, we have witnessed advances in both hardware and associated algorithms resulting in unprecedented access to volumes of 2D and, more recently, 3D data capturing human movement. We are no longer satisfied with recovering human pose as an image-space 2D skeleton, but seek to obtain a full 3D human body representation. The main challenges in acquiring 3D human shape from such raw measurements are identifying which parts of the data relate to body measurements and recovering from partial observations, often arising out of severe occlusion. For example, a person occluded by a piece of furniture, or being self-occluded in a profile view. In this paper, we propose POP, a novel and efficient paradigm for estimation and completion of human shape to produce a full parametric 3D model directly from single RGBD images, even under severe occlusion. At the heart of our method is a novel human body pose retrieval formulation that explicitly models and handles occlusion. The retrieved result is then refined by a robust optimization to yield a full representation of the human shape. We demonstrate our method on a range of challenging real world scenarios and produce high-quality results not possible by competing alternatives. The method opens up exciting AR/VR application possibilities by working on 'in-the-wild' measurements of human motion.
Reposing and Retargeting Unrigged Characters with Intrinsic-extrinsic Transfer
(The Eurographics Association, 2021) Musoni, Pietro; Marin, Riccardo; Melzi, Simone; Castellani, Umberto; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
In the 3D digital world, deformations and animations of shapes are fundamental topics for several applications. The entertainment industry, virtual and augmented reality, human-robot interactions are just some examples that pay attention to animation processes and related tools. In these contexts, researchers from several communities desire to govern deformations and animations of 3D geometries. This task is generally very complicated because it requires several skills covering different kinds of knowledge. For this reason, we propose a ready-to-use procedure to transfer a given animation from a source shape to a target shape that shares the same global structure. Our method proposes highly geometrical transferring, reposing, and retargeting, providing high-quality and efficient transfer, as shown in the qualitative evaluation that we report in the experimental section. The animation transfer we provide will potentially impact different scenarios, such as data augmentation for learning-based procedures or virtual avatar generation for orthopedic rehabilitation and social applications.
SHREC 2020 Track: Non-rigid Shape Correspondence of Physically-Based Deformations
(The Eurographics Association, 2020) Dyke, Roberto M.; Zhou, Feng; Lai, Yu-Kun; Rosin, Paul L.; Guo, Daoliang; Li, Kun; Marin, Riccardo; Yang, Jingyu; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
Commonly, novel non-rigid shape correspondence techniques focus on particular matching challenges. This can lead to the potential trade-off of poorer performance in other scenarios. An ideal dataset would provide a granular means for degrees of evaluation. In this paper, we propose a novel dataset of real scans that contain challenging non-isometric deformations to evaluate non-rigid point-to-point correspondence and registration algorithms. The deformations included in our dataset cover extreme types of physically-based contortions of a toy rabbit. Furthermore, shape pairs contain incrementally different types and amounts of deformation, this enables performance to be systematically evaluated with respect to the nature of the deformation. A brief investigation into different methods for initialising correspondence was undertaken, and a series of experiments were subsequently conducted to investigate the performance of state-of-the-art methods on the proposed dataset. We find that methods that rely on initial correspondences and local descriptors that are sensitive to local surface changes perform poorly in comparison to other strategies, and that a template-based approach performs the best.

Browsing by Author "Marin, Riccardo"

Results Per Page

Sort Options