38-Issue 2

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https://diglib.eg.org/handle/10.2312/2632738

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Accurate Synthesis of Multi-Class Disk Distributions
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Ecormier-Nocca, Pierre; Memari, Pooran; Gain, James; Cani, Marie-Paule; Alliez, Pierre and Pellacini, Fabio
While analysing and synthesising 2D distributions of points has been applied both to the generation of textures with discrete elements and for populating virtual worlds with 3D objects, the results are often inaccurate since the spatial extent of objects cannot be expressed.We introduce three improvements enabling the synthesis of more general distributions of elements. First, we extend continuous pair correlation function (PCF) algorithms to multi-class distributions using a dependency graph, thereby capturing interrelationships between distinct categories of objects. Second, we introduce a new normalised metric for disks, which makes the method applicable to both point and possibly overlapping disk distributions. The metric is specifically designed to distinguish perceptually salient features, such as disjoint, tangent, overlapping, or nested disks. Finally, we pay particular attention to convergence of the mean PCF as well as the validity of individual PCFs, by taking into consideration the variance of the input. Our results demonstrate that this framework can capture and reproduce real-life distributions of elements representing a variety of complex semi-structured patterns, from the interaction between trees and the understorey in a forest to droplets of water. More generally, it applies to any category of 2D object whose shape is better represented by bounding circles than points.
Character Navigation in Dynamic Environments Based on Optical Flow
(The Eurographics Association and John Wiley & Sons Ltd., 2019) López, Axel; Francois, Chaumette; Marchand, Eric; Pettré, Julien; Alliez, Pierre and Pellacini, Fabio
Steering and navigation are important components of character animation systems to enable them to autonomously move in their environment. In this work, we propose a synthetic vision model that uses visual features to steer agents through dynamic environments. Our agents perceive optical flow resulting from their relative motion with the objects of the environment. The optical flow is then segmented and processed to extract visual features such as the focus of expansion and time-to-collision. Then, we establish the relations between these visual features and the agent motion, and use them to design a set of control functions which allow characters to perform object-dependent tasks, such as following, avoiding and reaching. Control functions are then combined to let characters perform more complex navigation tasks in dynamic environments, such as reaching a goal while avoiding multiple obstacles. Agent's motion is achieved by local minimization of these functions. We demonstrate the efficiency of our approach through a number of scenarios. Our work sets the basis for building a character animation system which imitates human sensorimotor actions. It opens new perspectives to achieve realistic simulation of human characters taking into account perceptual factors, such as the lighting conditions of the environment.
Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Halperin, Tavi; Cain, Harel; Bibi, Ofir; Werman, Michael; Alliez, Pierre and Pellacini, Fabio
Digital videos such as those captured by a smartphone often exhibit exposure inconsistencies, a poorly exposed sky, or simply suffer from an uninteresting or plain looking sky. Professionals may edit these videos using advanced and time-consuming tools unavailable to most users, to replace the sky with a more expressive or imaginative sky. In this work, we propose an algorithm for automatic replacement of the sky region in a video with a different sky, providing nonprofessional users with a simple yet efficient tool to seamlessly replace the sky. The method is fast, achieving close to real-time performance on mobile devices and the user's involvement can remain as limited as simply selecting the replacement sky.
A CNN-based Flow Correction Method for Fast Preview
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Xiao, Xiangyun; Wang, Hui; Yang, Xubo; Alliez, Pierre and Pellacini, Fabio
Eulerian-based smoke simulations are sensitive to the initial parameters and grid resolutions. Due to the numerical dissipation on different levels of the grid and the nonlinearity of the governing equations, the differences in simulation resolutions will result in different results. This makes it challenging for artists to preview the animation results based on low-resolution simulations. In this paper, we propose a learning-based flow correction method for fast previewing based on low-resolution smoke simulations. The main components of our approach lie in a deep convolutional neural network, a grid-layer feature vector and a special loss function. We provide a novel matching model to represent the relationship between low-resolution and high-resolution smoke simulations and correct the overall shape of a low-resolution simulation to closely follow the shape of a high-resolution down-sampled version. We introduce the grid-layer concept to effectively represent the 3D fluid shape, which can also reduce the input and output dimensions. We design a special loss function for the fluid divergence-free constraint in the neural network training process. We have demonstrated the efficacy and the generality of our approach by simulating a diversity of animations deviating from the original training set. In addition, we have integrated our approach into an existing fluid simulation framework to showcase its wide applications.
Computational Design of Steady 3D Dissection Puzzles
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Tang, Keke; Song, Peng; Wang, Xiaofei; Deng, Bailin; Fu, Chi-Wing; Liu, Ligang; Alliez, Pierre and Pellacini, Fabio
Dissection puzzles require assembling a common set of pieces into multiple distinct forms. Existing works focus on creating 2D dissection puzzles that form primitive or naturalistic shapes. Unlike 2D dissection puzzles that could be supported on a tabletop surface, 3D dissection puzzles are preferable to be steady by themselves for each assembly form. In this work, we aim at computationally designing steady 3D dissection puzzles. We address this challenging problem with three key contributions. First, we take two voxelized shapes as inputs and dissect them into a common set of puzzle pieces, during which we allow slightly modifying the input shapes, preferably on their internal volume, to preserve the external appearance. Second, we formulate a formal model of generalized interlocking for connecting pieces into a steady assembly using both their geometric arrangements and friction. Third, we modify the geometry of each dissected puzzle piece based on the formal model such that each assembly form is steady accordingly. We demonstrate the effectiveness of our approach on a wide variety of shapes, compare it with the state-of-the-art on 2D and 3D examples, and fabricate some of our designed puzzles to validate their steadiness.
Controlling Motion Blur in Synthetic Long Time Exposures
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Lancelle, Marcel; Dogan, Pelin; Gross, Markus; Alliez, Pierre and Pellacini, Fabio
In a photo, motion blur can be used as an artistic style to convey motion and to direct attention. In panning or tracking shots, a moving object of interest is followed by the camera during a relatively long exposure. The goal is to get a blurred background while keeping the object sharp. Unfortunately, it can be difficult to impossible to precisely follow the object. Often, many attempts or specialized physical setups are needed. This paper presents a novel approach to create such images. For capturing, the user is only required to take a casually recorded hand-held video that roughly follows the object. Our algorithm then produces a single image which simulates a stabilized long time exposure. This is achieved by first warping all frames such that the object of interest is aligned to a reference frame. Then, optical flow based frame interpolation is used to reduce ghosting artifacts from temporal undersampling. Finally, the frames are averaged to create the result. As our method avoids segmentation and requires little to no user interaction, even challenging sequences can be processed successfully. In addition, artistic control is available in a number of ways. The effect can also be applied to create videos with an exaggerated motion blur. Results are compared with previous methods and ground truth simulations. The effectiveness of our method is demonstrated by applying it to hundreds of datasets. The most interesting results are shown in the paper and in the supplemental material.
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Kim, Byungsoo; Azevedo, Vinicius C.; Thuerey, Nils; Kim, Theodore; Gross, Markus; Solenthaler, Barbara; Alliez, Pierre and Pellacini, Fabio
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.
Deep HDR Video from Sequences with Alternating Exposures
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Kalantari, Nima Khademi; Ramamoorthi, Ravi; Alliez, Pierre and Pellacini, Fabio
A practical way to generate a high dynamic range (HDR) video using off-the-shelf cameras is to capture a sequence with alternating exposures and reconstruct the missing content at each frame. Unfortunately, existing approaches are typically slow and are not able to handle challenging cases. In this paper, we propose a learning-based approach to address this difficult problem. To do this, we use two sequential convolutional neural networks (CNN) to model the entire HDR video reconstruction process. In the first step, we align the neighboring frames to the current frame by estimating the flows between them using a network, which is specifically designed for this application. We then combine the aligned and current images using another CNN to produce the final HDR frame. We perform an end-to-end training by minimizing the error between the reconstructed and ground truth HDR images on a set of training scenes. We produce our training data synthetically from existing HDR video datasets and simulate the imperfections of standard digital cameras using a simple approach. Experimental results demonstrate that our approach produces high-quality HDR videos and is an order of magnitude faster than the state-of-the-art techniques for sequences with two and three alternating exposures.
Deep Video-Based Performance Cloning
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Aberman, Kfir; Shi, Mingyi; Liao, Jing; Lischinski, Dani; Chen, Baoquan; Cohen-Or, Daniel; Alliez, Pierre and Pellacini, Fabio
We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space-time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses paired training data, self-generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. Through data augmentation, our network is able to synthesize images of the target actor in poses never captured by the reference video. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances.
Design and Automated Generation of Japanese Picture Puzzles
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Kerkhof, Mees van de; Jong, Tim de; Parment, Raphael; Löffler, Maarten; Vaxman, Amir; van Kreveld, Marc; Alliez, Pierre and Pellacini, Fabio
We introduce the generalized nonogram, an extension of the well-known nonogram or Japanese picture puzzle. It is not based on a regular square grid but on a subdivision (arrangement) with differently shaped cells, bounded by straight lines or curves. To generate a good, clear puzzle from a filled line drawing, the arrangement that is formed for the puzzle must meet a number of criteria. Some of these relate to the puzzle and some to the geometry. We give an overview of these criteria and show that a puzzle can be generated by an optimization method like simulated annealing. Experimentally, we analyze the convergence of the method and the remaining penalty score on several input pictures along with various other design options.
Dual Sheet Meshing: An Interactive Approach to Robust Hexahedralization
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Takayama, Kenshi; Alliez, Pierre and Pellacini, Fabio
The combinatorial dual of a hex mesh induces a collection of mutually intersecting surfaces (dual sheets). Inspired by Campen et al.'s work on quad meshing [CBK12,CK14], we propose to directly generate such dual sheets so that, as long as the volume is properly partitioned by the dual sheets, we are guaranteed to arrive at a valid all-hex mesh topology. Since automatically generating dual sheets seems much harder than the 2D counterpart, we chose to leave the task to the user; our system is equipped with a few simple 3D modeling tools for interactively designing dual sheets. Dual sheets are represented as implicit surfaces in our approach, greatly simplifying many of the computational steps such as finding intersections and analyzing topology. We also propose a simple algorithm for primalizing the dual graph where each dual cell, often enclosing singular edges, gets mapped onto a reference polyhedron via harmonic parameterization. Preservation of sharp features is simply achieved by modifying the boundary conditions. We demonstrate the feasibility of our approach through various modeling examples.
Dynamic Visibility-Driven Molecular Surfaces
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Bruckner, Stefan; Alliez, Pierre and Pellacini, Fabio
Molecular surface representations are an important tool for the visual analysis of molecular structure and function. In this paper, we present a novel method for the visualization of dynamic molecular surfaces based on the Gaussian model. In contrast to previous approaches, our technique does not rely on the construction of intermediate representations such as grids or triangulated surfaces. Instead, it operates entirely in image space, which enables us to exploit visibility information to efficiently skip unnecessary computations. With this visibility-driven approach, we can visualize dynamic high-quality surfaces for molecules consisting of millions of atoms. Our approach requires no preprocessing, allows for the interactive adjustment of all properties and parameters, and is significantly faster than previous approaches, while providing superior quality.
Elastic Correspondence between Triangle Meshes
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Ezuz, Danielle; Heeren, Behrend; Azencot, Omri; Rumpf, Martin; Ben-Chen, Mirela; Alliez, Pierre and Pellacini, Fabio
We propose a novel approach for shape matching between triangular meshes that, in contrast to existing methods, can match crease features. Our approach is based on a hybrid optimization scheme, that solves simultaneously for an elastic deformation of the source and its projection on the target. The elastic energy we minimize is invariant to rigid body motions, and its non-linear membrane energy component favors locally injective maps. Symmetrizing this model enables feature aligned correspondences even for non-isometric meshes. We demonstrate the advantage of our approach over state of the art methods on isometric and non-isometric datasets, where we improve the geodesic distance from the ground truth, the conformal and area distortions, and the mismatch of the mean curvature functions. Finally, we show that our computed maps are applicable for surface interpolation, consistent cross-field computation, and consistent quadrangular remeshing of a set of shapes.
EUROGRAPHICS 2019: CGF 38-2 Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Alliez, Pierre; Pellacini, Fabio; Alliez, Pierre and Pellacini, Fabio
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Exact Constraint Satisfaction for Truly Seamless Parametrization
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Mandad, Manish; Campen, Marcel; Alliez, Pierre and Pellacini, Fabio
In the field of global surface parametrization a recent focus has been on so-called seamless parametrization. This term refers to parametrization approaches which, while using an atlas of charts to enable the handling of surfaces of arbitrary topology, relate the parametrization across the cuts between charts via transition functions from special classes of transformations. This effectively makes the cuts invisible to applications which are invariant to these specific transformations in some sense. In actual implementations of these parametrization approaches, however, these restrictions are obeyed only approximately; errors stem from the tolerances of numerical solvers employed and, ultimately, from the limited accuracy of floating point arithmetic. In practice, robustness issues arise from these flaws in the seamlessness of a parametrization, no matter how small. We present a robust global algorithm that turns a given approximately seamless parametrization into an exactly seamless one - that still is representable by standard floating point numbers. It supports common practically relevant additional constraints regarding boundary and feature curve alignment or isocurve connectivity, and ensures that these are likewise fulfilled exactly. This allows subsequent algorithms to operate robustly on the resulting truly seamless parametrization. We believe that the core of our method will furthermore be of benefit in a broader range of applications involving linearly constrained numerical optimization.
Exploratory Stage Lighting Design using Visual Objectives
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Shimizu, Evan; Paris, Sylvain; Fisher, Matthew; Yumer, Ersin; Fatahalian, Kayvon; Alliez, Pierre and Pellacini, Fabio
Lighting is a critical element of theater. A lighting designer is responsible for drawing the audience's attention to a specific part of the stage, setting time of day, creating a mood, and conveying emotions. Designers often begin the lighting design process by collecting reference visual imagery that captures different aspects of their artistic intent. Then, they experiment with various lighting options to determine which ideas work best on stage. However, modern stages contain tens to hundreds of lights, and setting each light source's parameters individually to realize an idea is both tedious and requires expert skill. In this paper, we describe an exploratory lighting design tool based on feedback from professional designers. The system extracts abstract visual objectives from reference imagery and applies them to target regions of the stage. Our system can rapidly generate plausible design candidates that embody the visual objectives through a Gibbs sampling method, and present them as a design gallery for rapid exploration and iterative refinement. We demonstrate that the resulting system allows lighting designers of all skill levels to quickly create and communicate complex designs, even for scenes containing many color-changing lights.
Generating Color Scribble Images using Multi-layered Monochromatic Strokes Dithering
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Lo, Yi-Hsiang; Lee, Ruen-Rone; Chu, Hung-Kuo; Alliez, Pierre and Pellacini, Fabio
Color scribbling is a unique form of illustration where artists use compact, overlapping, and monochromatic scribbles at microscopic scale to create astonishing colorful images at macroscopic scale. The creation process is skill-demanded and time-consuming, which typically involves drawing monochromatic scribbles layer-by-layer to depict true-color subjects using a limited color palette delicately. In this work, we present a novel computational framework for automatic generation of color scribble images from arbitrary raster images. The core contribution of our work lies in a novel color dithering model tailormade for synthesizing a smooth color appearance using multiple layers of overlapped monochromatic strokes. Specifically, our system reconstructs the appearance of the input image by (i) generating layers of monochromatic scribbles based on a limited color palette derived from input image, and (ii) optimizing the drawing sequence among layers to minimize the visual color dissimilarity between dithered image and original image as well as the color banding artifacts. We demonstrate the effectiveness and robustness of our algorithm with various convincing results synthesized from a variety of input images with different stroke patterns. The experimental study further shows that our approach faithfully captures the scribble style and the color presentation at respectively microscopic and macroscopic scales, which is otherwise difficult for state-of-the-art methods.
Generating Stochastic Wall Patterns On-the-fly with Wang Tiles
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Derouet-Jourdan, Alexandre; Salvati, Marc; Jonchier, Théo; Alliez, Pierre and Pellacini, Fabio
The game and movie industries always face the challenge of reproducing materials. This problem is tackled by combining illumination models and various textures (painted or procedural patterns). Generating stochastic wall patterns is crucial in the creation of a wide range of backgrounds (castles, temples, ruins...). A specific Wang tile set was introduced previously to tackle this problem, in an iterative fashion. However, long lines may appear as visual artifacts. We use this tile set in a new on-the-fly procedure to generate stochastic wall patterns. For this purpose, we introduce specific hash functions implementing a constrained Wang tiling. This technique makes possible the generation of boundless textures while giving control over the maximum line length. The algorithm is simple and easy to implement, and the wall structure we get from the tiles allows to achieve visuals that reproduce all the small details of artist painted walls.
A Geometrically Consistent Viscous Fluid Solver with Two-Way Fluid-Solid Coupling
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Takahashi, Tetsuya; Lin, Ming C.; Alliez, Pierre and Pellacini, Fabio
We present a grid-based fluid solver for simulating viscous materials and their interactions with solid objects. Our method formulates the implicit viscosity integration as a minimization problem with consistently estimated volume fractions to account for the sub-grid details of free surfaces and solid boundaries. To handle the interplay between fluids and solid objects with viscosity forces, we also formulate the two-way fluid-solid coupling as a unified minimization problem based on the variational principle, which naturally enforces the boundary conditions. Our formulation leads to a symmetric positive definite linear system with a sparse matrix regardless of the monolithically coupled solid objects. Additionally, we present a position-correction method using density constraints to enforce the uniform distributions of fluid particles and thus prevent the loss of fluid volumes. We demonstrate the effectiveness of our method in a wide range of viscous fluid scenarios.
Geometry Aware Tori Decomposition
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Chen, Jia; Gopi, Meenakshisundaram; Alliez, Pierre and Pellacini, Fabio
This work presents a shape decomposition algorithm to partition a complex high genus surface into simple primitives, each of which is a torus. First, using a novel iterative algorithm, handle and tunnel fundamental cycles on the surface are progressively localized. Then, the problem of computing the splitting cycles that produce such a tori decomposition is posed as a min-cut problem on the mesh's dual graph with earlier computed tunnels as source and target. The edge weights for the min-cut problem are designed for the cut to be geometry-aware. We present an implementation and demonstrate the results of our algorithm on numerous examples.

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