Computational caricaturization of surfaces

Sela, Matan; Aflalo, Yonathan; Kimmel, Ron

doi:10.1016/j.cviu.2015.05.013

Cited by 27 publications

(23 citation statements)

References 13 publications

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“…Our second simulation study modifies computed tomography (CT) scans of real Lemuridae teeth (one of the five families of Strepsirrhini primates commonly known as lemurs) [10] using a well-known caricatur-ization procedure [22]. We fix the triangular mesh of an individual tooth and specify class-specific regions centered around known biological landmarks (Fig.…”

Section: Resultsmentioning

confidence: 99%

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

Wang

Sudijono

Kirveslahti

et al. 2019

Preprint

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22The recent curation of large-scale databases with 3D surface scans of shapes has motivated the devel-23 opment of tools that better detect global-patterns in morphological variation. Studies which focus on 24 identifying differences between shapes have been limited to simple pairwise comparisons and rely on 25 pre-specified landmarks (that are often known). We present SINATRA: the first statistical pipeline for 26 analyzing collections of shapes without requiring any correspondences. Our novel algorithm takes in two 27 classes of shapes and highlights the physical features that best describe the variation between them. We 28 use a rigorous simulation framework to assess our approach. Lastly, as a case study, we use SINATRA to 29 analyze mandibular molars from four different suborders of primates and demonstrate its ability recover 30 known morphometric variation across phylogenies. 31 Introduction 32 Sub-image analysis is an important open problem in both medical imaging studies and geometric mor-33 phometric applications. The problem asks which physical features of shapes are most important for 34 differentiating between two classes of 3D images or shapes such as computed tomography (CT) scans of 35 bones or magnetic resonance images (MRI) of different tissues. More generally, the sub-image analysis 36problem can be framed as a regression-based task: given a collection of shapes, find the properties that 37 explain the greatest variation in some response variable (continuous or binary). One example is identify-38 ing the structures of glioblastoma tumors that best indicate signs of potential relapse and other clinical 39 outcomes [1]. From a statistical perspective, the sub-image selection problem is directly related to the 40 variable selection problem -given high-dimensional covariates and a univariate outcome, we want to 41 infer which variables are most relevant in explaining or predicting variation in the observed response. 42 2Framing sub-image analysis as a regression presents several challenges. The first challenge centers 43 around representing a 3D object as a (square integrable) covariate or feature vector. The transformation 44 should lose a minimum amount of geometric information and apply to a wide range of shape and imaging 45 datasets. In this paper, we use a tool from integral geometry and differential topology called the Eu-46 ler characteristic (EC) transform [1][2][3][4], which maps shapes into vectors without requiring pre-specified 47 landmark points or pairwise correspondences. This property is central to our innovations. 48After finding a vector representation of the shape, our second challenge is quantifying which topological 49 features are most relevant in explaining variation in a continuous outcome or binary class label. We 50 address this classic take on variable selection by using a Bayesian regression model and an information 51 theoretic metric to measure the relevance of each topological feature. Our Bayesian method allows us 52 to perform variable selection for nonlinear func...

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Section: Resultsmentioning

confidence: 99%

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

Wang

Sudijono

Kirveslahti

et al. 2019

Preprint

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“…Shape Exaggeration. Shape exaggeration is based on the method in (Sela et al 2015). Given a mesh, it first applies a scaling factor to the gradients at vertices, and then reconstructs the 3D shape from these new gradients using the method in (Yu et al 2004).…”

Section: Database Constructionmentioning

confidence: 99%

DeepSketch2Face

Han

Gao

2017

ACM Trans. Graph.

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Fig. 1. Using our sketching system, an amateur user can create 3D face or caricature models with complicated shape and expression in a few minutes. Both models shown here were created in less than 10 minutes by a user without any prior drawing and modeling experiences.Face modeling has been paid much attention in the field of visual computing. There exist many scenarios, including cartoon characters, avatars for social media, 3D face caricatures as well as face-related art and design, where low-cost interactive face modeling is a popular approach especially among amateur users. In this paper, we propose a deep learning based sketching system for 3D face and caricature modeling. This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features. A novel CNN based deep regression network is designed for inferring 3D face models from 2D sketches. Our network fuses both CNN and shape based features of the input sketch, and has two independent branches of fully connected layers generating independent subsets of coefficients for a bilinear face representation. Our system also supports gesture based interactions for users to further manipulate initial face models. Both user studies and numerical results indicate that our sketching system can help users create face models quickly and effectively. A significantly expanded face database with diverse identities, expressions and levels of exaggeration is constructed to promote further research and evaluation of face modeling techniques.

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“…(13) e value of γ depends on the reliability of the landmarks, if the landmarks are not accurate then γ should be small. To demonstrate the e ectiveness of this approach, we used as input the Homer and Max Planck models, and caricatures of these models generated using the method by Sela et al [2015]. e caricatures have the same triangulation as the original shapes.…”

Section: Dataset: Caricatures Input: Landmarksmentioning

confidence: 99%

Reversible Harmonic Maps between Discrete Surfaces

2019

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Information transfer between triangle meshes is of great importance in computer graphics and geometry processing. To facilitate this process, a smooth and accurate map is typically required between the two meshes. While such maps can sometimes be computed between nearly-isometric meshes, the more general case of meshes with diverse geometries remains challenging. We propose a novel approach for direct map computation between triangle meshes without mapping to an intermediate domain, which optimizes for the harmonicity and reversibility of the forward and backward maps. Our method is general both in the information it can receive as input, e.g. point landmarks, a dense map or a functional map, and in the diversity of the geometries to which it can be applied. We demonstrate that our maps exhibit lower conformal distortion than the state-of-the-art, while succeeding in correctly mapping key features of the input shapes.ACM Reference format:

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Computational caricaturization of surfaces

Cited by 27 publications

References 13 publications

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

DeepSketch2Face

Reversible Harmonic Maps between Discrete Surfaces

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