38-Issue 2

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

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A Low-Dimensional Function Space for Efficient Spectral Upsampling
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Jakob, Wenzel; Hanika, Johannes; Alliez, Pierre and Pellacini, Fabio
We present a versatile technique to convert textures with tristimulus colors into the spectral domain, allowing such content to be used in modern rendering systems. Our method is based on the observation that suitable reflectance spectra can be represented using a low-dimensional parametric model that is intrinsically smooth and energy-conserving, which leads to significant simplifications compared to prior work. The resulting spectral textures are compact and efficient: storage requirements are identical to standard RGB textures, and as few as six floating point instructions are required to evaluate them at any wavelength. Our model is the first spectral upsampling method to achieve zero error on the full sRGB gamut. The technique also supports large-gamut color spaces, and can be vectorized effectively for use in rendering systems that handle many wavelengths at once.
Object Partitioning for Support-Free 3D-Printing
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Karasik, Eli; Fattal, Raanan; Werman, Michael; Alliez, Pierre and Pellacini, Fabio
Fused deposition modeling based 3D-printing is becoming increasingly popular due to it's low-cost and simple operation and maintenance. While it produces rugged prints made from a wide range of materials, it suffers from an inherent printing limitation where it cannot produce overhanging surfaces of non-trivial size. This limitation can be handled by constructing temporary support-structures, however this solution involves additional material costs, longer print time, and often a fair amount of labor in removing it. In this paper we present a new method for partitioning general solid objects into a small number of parts that can be printed with no support. The partitioning is computed by applying a sequence of cutting-planes that split the object recursively. Unlike existing algorithms, the planes are not chosen at random, rather they are derived from shape analysis routines that identify and resolve various commonly-found geometric configurations. In addition, we guide this search by a revised set of conditions that both ensure the objects' printability as well as realistically model the printing capabilities of the printer at hand. Evaluation of the new method demonstrates its ability to efficiently obtain support-free partitionings typically containing fewer parts compared to existing methods that rely on support-structures.
Neural BTF Compression and Interpolation
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Rainer, Gilles; Jakob, Wenzel; Ghosh, Abhijeet; Weyrich, Tim; Alliez, Pierre and Pellacini, Fabio
The Bidirectional Texture Function (BTF) is a data-driven solution to render materials with complex appearance. A typical capture contains tens of thousands of images of a material sample under varying viewing and lighting conditions.While capable of faithfully recording complex light interactions in the material, the main drawback is the massive memory requirement, both for storing and rendering, making effective compression of BTF data a critical component in practical applications. Common compression schemes used in practice are based on matrix factorization techniques, which preserve the discrete format of the original dataset. While this approach generalizes well to different materials, rendering with the compressed dataset still relies on interpolating between the closest samples. Depending on the material and the angular resolution of the BTF, this can lead to blurring and ghosting artefacts. An alternative approach uses analytic model fitting to approximate the BTF data, using continuous functions that naturally interpolate well, but whose expressive range is often not wide enough to faithfully recreate materials with complex non-local lighting effects (subsurface scattering, inter-reflections, shadowing and masking...). In light of these observations, we propose a neural network-based BTF representation inspired by autoencoders: our encoder compresses each texel to a small set of latent coefficients, while our decoder additionally takes in a light and view direction and outputs a single RGB vector at a time. This allows us to continuously query reflectance values in the light and view hemispheres, eliminating the need for linear interpolation between discrete samples. We train our architecture on fabric BTFs with a challenging appearance and compare to standard PCA as a baseline. We achieve competitive compression ratios and high-quality interpolation/extrapolation without blurring or ghosting artifacts.
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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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.
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.
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.
A Subspace Method for Fast Locally Injective Harmonic Mapping
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Hefetz, Eden Fedida; Chien, Edward; Weber, Ofir; Alliez, Pierre and Pellacini, Fabio
We present a fast algorithm for low-distortion locally injective harmonic mappings of genus 0 triangle meshes with and without cone singularities. The algorithm consists of two portions, a linear subspace analysis and construction, and a nonlinear nonconvex optimization for determination of a mapping within the reduced subspace. The subspace is the space of solutions to the Harmonic Global Parametrization (HGP) linear system [BCW17], and only vertex positions near cones are utilized, decoupling the variable count from the mesh density. A key insight shows how to construct the linear subspace at a cost comparable to that of a linear solve, extracting a very small set of elements from the inverse of the matrix without explicitly calculating it. With a variable count on the order of the number of cones, a tangential alternating projection method [HCW17] and a subsequent Newton optimization [CW17] are used to quickly find a low-distortion locally injective mapping. This mapping determination is typically much faster than the subspace construction. Experiments demonstrating its speed and efficacy are shown, and we find it to be an order of magnitude faster than HGP and other alternatives.
Learning to Importance Sample in Primary Sample Space
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Zheng, Quan; Zwicker, Matthias; Alliez, Pierre and Pellacini, Fabio
Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. We propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the renderer using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving (''Real NVP'') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with an existing rendering technique by treating it as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.
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.
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.
Multi-Pose Interactive Linkage Design
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Nishida, Gen; Bousseau, Adrien; Aliaga, Daniel; Alliez, Pierre and Pellacini, Fabio
We introduce an interactive tool for novice users to design mechanical objects made of 2.5D linkages. Users simply draw the shape of the object and a few key poses of its multiple moving parts. Our approach automatically generates a one-degree-offreedom linkage that connects the fixed and moving parts, such that the moving parts traverse all input poses in order without any collision with the fixed and other moving parts. In addition, our approach avoids common linkage defects and favors compact linkages and smooth motion trajectories. Finally, our system automatically generates the 3D geometry of the object and its links, allowing the rapid creation of a physical mockup of the designed object.
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.
Learning a Generative Model for Multi-Step Human-Object Interactions from Videos
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Wang, He; Pirk, Sören; Yumer, Ersin; Kim, Vladimir; Sener, Ozan; Sridhar, Srinath; Guibas, Leonidas; Alliez, Pierre and Pellacini, Fabio
Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well-accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi-step dynamic human-object interactions. Generating multi-step interactions is challenging since the space of such interactions is exponential in the number of objects, activities, and time steps. We propose to handle this combinatorial complexity by learning a lower dimensional space of plausible human-object interactions. We use action plots to represent interactions as a sequence of discrete actions along with the participating objects and their states. To build action plots, we present an automatic method that uses state-of-the-art computer vision techniques on RGB videos in order to detect individual objects and their states, extract the involved hands, and recognize the actions performed. The action plots are built from observing videos of everyday activities and are used to train a generative model based on a Recurrent Neural Network (RNN). The network learns the causal dependencies and constraints between individual actions and can be used to generate novel and diverse multi-step human-object interactions. Our representation and generative model allows new capabilities in a variety of applications such as interaction prediction, animation synthesis, and motion planning for a real robotic agent.
Gradient Outlier Removal for Gradient-Domain Path Tracing
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Ha, Saerom; Oh, Sojin; Back, Jonghee; Yoon, Sung-Eui; Moon, Bochang; Alliez, Pierre and Pellacini, Fabio
We present a new outlier removal technique for a gradient-domain path tracing (G-PT) that computes image gradients as well as colors. Our approach rejects gradient outliers whose estimated errors are much higher than those of the other gradients for improving reconstruction quality for the G-PT. We formulate our outlier removal problem as a least trimmed squares optimization, which employs only a subset of gradients so that a final image can be reconstructed without including the gradient outliers. In addition, we design this outlier removal process so that the chosen subset of gradients maintains connectivity through gradients between pixels, preventing pixels from being isolated. Lastly, the optimal number of inlier gradients is estimated to minimize our reconstruction error. We have demonstrated that our reconstruction with robustly rejecting gradient outliers produces visually and numerically improved results, compared to the previous screened Poisson reconstruction that uses all the gradients.
Hierarchical Rasterization of Curved Primitives for Vector Graphics Rendering on the GPU
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Dokter, Mark; Hladký, Jozef; Parger, Mathias; Schmalstieg, Dieter; Seidel, Hans-Peter; Steinberger, Markus; Alliez, Pierre and Pellacini, Fabio
In this paper, we introduce the CPatch, a curved primitive that can be used to construct arbitrary vector graphics. A CPatch is a generalization of a 2D polygon: Any number of curves up to a cubic degree bound a primitive. We show that a CPatch can be rasterized efficiently in a hierarchical manner on the GPU, locally discarding irrelevant portions of the curves. Our rasterizer is fast and scalable, works on all patches in parallel, and does not require any approximations. We show a parallel implementation of our rasterizer, which naturally supports all kinds of color spaces, blending and super-sampling. Additionally, we show how vector graphics input can efficiently be converted to a CPatch representation, solving challenges like patch self-intersections and false inside-outside classification. Results indicate that our approach is faster than the state-of-the-art, more flexible and could potentially be implemented in hardware.
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.
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.
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.
Local Editing of Procedural Models
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Lipp, Markus; Specht, Matthias; Lau, Cheryl; Wonka, Peter; Mueller, Pascal; Alliez, Pierre and Pellacini, Fabio
Procedural modeling is used across many industries for rapid 3D content creation. However, professional procedural tools often lack artistic control, requiring manual edits on baked results, diminishing the advantages of a procedural modeling pipeline. Previous approaches to enable local artistic control require special annotations of the procedural system and manual exploration of potential edit locations. Therefore, we propose a novel approach to discover meaningful and non-redundant good edit locations (GELs). We introduce a bottom-up algorithm for finding GELs directly from the attributes in procedural models, without special annotations. To make attribute edits at GELs persistent, we analyze their local spatial context and construct a meta-locator to uniquely specify their structure. Meta-locators are calculated independently per attribute, making them robust against changes in the procedural system. Functions on meta-locators enable intuitive and robust multi-selections. Finally, we introduce an algorithm to transfer meta-locators to a different procedural model. We show that our approach greatly simplifies the exploration of the local edit space, and we demonstrate its usefulness in a user study and multiple real-world examples.

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