37-Issue 7

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https://diglib.eg.org/handle/10.2312/2632664

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Reconstructing Flowers from Sketches
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Bobenrieth, Cédric; Seo, Hyewon; Cordier, Frédéric; Habibi, Arash; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
As the symbol of beauty, floral objects have been one of the most popular subjects of artistic drawing. However, designing 3D floral models is generally time- and resource-consuming, because of their structural and geometrical complexity. In this paper, we address the problem of reconstructing floral objects from sketch input. The user draws a relatively clean sketch of a flower and a few additional guide markings from an arbitrary view to rapidly create quality geometric models of flowers. Our system offers a novel modeling scheme compared to several existing flower modelers accepting sketch as input, where the user is required to work with different views, providing step-by-step sketch input. Given the silhouette and the guide strokes, an assumed, common botanical structure is estimated, i.e. a cone for each ring of petals. The cones and the silhouette sketch that we segment into elementary curves are used to retrieve model elements from the pre-constructed shape database. These elements are then placed together around the cone, where an additional, per-element deformation is performed so as to maximize the silhouette similarity between the user sketch and the 3D flower model from the chosen view. Our system has shown to robustly create a variety of flowers in various configurations, including flower models with several petal layers and various blooming degrees, drawn from different views.
Ellipsoid Packing Structures on Freeform Surfaces
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Xu, Qun-Ce; Deng, Bailin; Yang, Yong-Liang; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Designers always get good inspirations from fascinating geometric structures gifted by the nature. In the recent years, various computational design tools have been proposed to help generate cell packing structures on freeform surfaces, which consist of a packing of simple primitives, such as polygons, spheres, etc. In this work, we aim at computationally generating novel ellipsoid packing structures on freeform surfaces. We formulate the problem as a generalization of sphere packing structures in the sense that anisotropic ellipsoids are used instead of isotropic spheres to pack a given surface. This is done by defining an anisotropic metric based on local surface anisotropy encoded by principal curvatures and the corresponding directions. We propose an optimization framework that can optimize the shapes of individual ellipsoids and the spatial relation between neighboring ellipsoids to form a quality packing structure. A tailored anisotropic remeshing method is also employed to better initialize the optimization and ensure the quality of the result. Our framework is extensively evaluated by optimizing ellipsoid packing and generating appealing geometric structures on a variety of freeform surfaces.
Skeletex: Skeleton-texture Co-representation for Topology-driven Real-time Interchange and Manipulation of Surface Regions
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Madaras, Martin; Riecický, Adam; Mesároš, Michal; Stuchlík, Martin; Piovarči, Michal; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Mesh processing algorithms depend on quick access to the local neighborhood, which requires costly memory queries. Moreover, even having access to the local neighborhood is not enough to efficiently perform many geometry processing algorithms in an automatic or semi-automatic way. As humans, we often imagine mesh editing at the level of topological information, e.g., altering surface features, adding limbs, etc., which is not supported by current data structures. These limitations come from the widely used mesh representations because the needed information is not implicitly defined by the structure. We propose a novel model representation called Skeletex. Each 3D model is decomposed into two elements: a skeletal structure that encodes the model topology and a vector displacement map to capture fine details of the geometry. Such a co-representation contains the topology information, as well as the information about the local vertex neighborhood at each texel. Additionally, our data structure facilitates an automatic skeleton-based cross-parameterization. This allows us to implement the mesh manipulation tasks in parallel, using a unified streamlined pipeline that directly maps to the GPU. We demonstrate the capabilities of our data structure by implementing surface region transfer and mesh morphing of 3D models.
Automatic Mechanism Modeling from a Single Image with CNNs
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Lin, Minmin; Shao, Tianjia; Zheng, Youyi; Ren, Zhong; Weng, Yanlin; Yang, Yin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
This paper presents a novel system that enables a fully automatic modeling of both 3D geometry and functionality of a mechanism assembly from a single RGB image. The resulting 3D mechanism model highly resembles the one in the input image with the geometry, mechanical attributes, connectivity, and functionality of all the mechanical parts prescribed in a physically valid way. This challenging task is realized by combining various deep convolutional neural networks to provide high-quality and automatic part detection, segmentation, camera pose estimation and mechanical attributes retrieval for each individual part component. On the top of this, we use a local/global optimization algorithm to establish geometric interdependencies among all the parts while retaining their desired spatial arrangement. We use an interaction graph to abstract the inter-part connection in the resulting mechanism system. If an isolated component is identified in the graph, our system enumerates all the possible solutions to restore the graph connectivity, and outputs the one with the smallest residual error. We have extensively tested our system with a wide range of classic mechanism photos, and experimental results show that the proposed system is able to build high-quality 3D mechanism models without user guidance.
A Practical Approach to Physically-Based Reproduction of Diffusive Cosmetics
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Goanghun; Ko, Hyeong-Seok; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper, we introduce so-called the bSX method as a new way to utilize the Kubelka-Munk (K-M) model. Assuming the material is completely diffusive, the K-M model gives the reflectance and transmittance of the material from the observation of the material applied on a backing, where the observation includes the thickness of the material application. By rearranging the original K-M equation, we propose that the reflectance and transmittance can be calculated without knowing the thickness. This is a practically useful contribution. Based on the above finding, we develop the bSX method which can (1) capture the material specific parameters from the two photos - taken before and after the material application, and (2) reproduce its effect on a novel backing. We experimented the proposed method in various cases related to virtual cosmetic try-on, which include (1) capture from a single color backing, (2) capture from human skin backing, (3) reproduction of varying thickness effect, (4) reproduction of multi-layer cosmetic application effect, (5) applying the proposed method to makeup transfer. Compared to previous image-based makeup transfer methods, the bSX method reproduces the feel of the cosmetics more accurately.
Fast Global Illumination with Discrete Stochastic Microfacets Using a Filterable Model
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wang, Beibei; Wang, Lu; Holzschuch, Nicolas; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Many real-life materials have a sparkling appearance, whether by design or by nature. Examples include metallic paints, sparkling varnish but also snow. These sparkles correspond to small, isolated, shiny particles reflecting light in a specific direction, on the surface or embedded inside the material. The particles responsible for these sparkles are usually small and discontinuous. These characteristics make it diffcult to integrate them effciently in a standard rendering pipeline, especially for indirect illumination. Existing approaches use a 4-dimensional hierarchy, searching for light-reflecting particles simultaneously in space and direction. The approach is accurate, but still expensive. In this paper, we show that this 4-dimensional search can be approximated using separate 2-dimensional steps. This approximation allows fast integration of glint contributions for large footprints, reducing the extra cost associated with glints be an order of magnitude.
Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Jeon, Junho; Jung, Jinwoong; Kim, Jungeon; Lee, Seungyong; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Semantic segmentation partitions a given image or 3D model of a scene into semantically meaning parts and assigns predetermined labels to the parts. With well-established datasets, deep networks have been successfully used for semantic segmentation of RGB and RGB-D images. On the other hand, due to the lack of annotated large-scale 3D datasets, semantic segmentation for 3D scenes has not yet been much addressed with deep learning. In this paper, we present a novel framework for generating semantically segmented triangular meshes of reconstructed 3D indoor scenes using volumetric semantic fusion in the reconstruction process. Our method integrates the results of CNN-based 2D semantic segmentation that is applied to the RGB-D stream used for dense surface reconstruction. To reduce the artifacts from noise and uncertainty of single-view semantic segmentation, we introduce adaptive integration for the volumetric semantic fusion and CRF-based semantic label regularization. With these methods, our framework can easily generate a high-quality triangular mesh of the reconstructed 3D scene with dense (i.e., per-vertex) semantic labels. Extensive experiments demonstrate that our semantic segmentation results of 3D scenes achieves the state-of-the-art performance compared to the previous voxel-based and point cloud-based methods.
Instant Stippling on 3D Scenes
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Ma, Lei; Guo, Jianwei; Yan, Dong-Ming; Sun, Hanqiu; Chen, Yanyun; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper, we present a novel real-time approach to generate high-quality stippling on 3D scenes. The proposed method is built on a precomputed 2D sample sequence called incremental Voronoi set with blue-noise properties. A rejection sampling scheme is then applied to achieve tone reproduction, by thresholding the sample indices proportional to the inverse target tonal value to produce a suitable stipple density. Our approach is suitable for stippling large-scale or even dynamic scenes because the thresholding of individual stipples is trivially parallelizable. In addition, the static nature of the underlying sequence benefits the frame-to-frame coherence of the stippling. Finally, we propose an extension that supports stipples of varying sizes and tonal values, leading to smoother spatial and temporal transitions. Experimental results reveal that the temporal coherence and real-time performance of our approach are superior to those of previous approaches.
FashionGAN: Display your fashion design using Conditional Generative Adversarial Nets
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Cui, Yi Rui; Liu, Qi; Gao, Cheng Ying; Su, Zhuo; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Virtual garment display plays an important role in fashion design for it can directly show the design effect of the garment without having to make a sample garment like traditional clothing industry. In this paper, we propose an end-to-end virtual garment display method based on Conditional Generative Adversarial Networks. Different from existing 3D virtual garment methods which need complex interactions and domain-specific user knowledge, our method only need users to input a desired fashion sketch and a specified fabric image then the image of the virtual garment whose shape and texture are consistent with the input fashion sketch and fabric image can be shown out quickly and automatically. Moreover, it can also be extended to contour images and garment images, which further improves the reuse rate of fashion design. Compared with the existing image-to-image methods, the quality of images generated by our method is better in terms of color and shape.
Deep Video Stabilization Using Adversarial Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Xu, Sen-Zhe; Hu, Jun; Wang, Miao; Mu, Tai-Jiang; Hu, Shi-Min; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Video stabilization is necessary for many hand-held shot videos. In the past decades, although various video stabilization methods were proposed based on the smoothing of 2D, 2.5D or 3D camera paths, hardly have there been any deep learning methods to solve this problem. Instead of explicitly estimating and smoothing the camera path, we present a novel online deep learning framework to learn the stabilization transformation for each unsteady frame, given historical steady frames. Our network is composed of a generative network with spatial transformer networks embedded in different layers, and generates a stable frame for the incoming unstable frame by computing an appropriate affine transformation. We also introduce an adversarial network to determine the stability of a piece of video. The network is trained directly using the pair of steady and unsteady videos. Experiments show that our method can produce similar results as traditional methods, moreover, it is capable of handling challenging unsteady video of low quality, where traditional methods fail, such as video with heavy noise or multiple exposures. Our method runs in real time, which is much faster than traditional methods.
Curvature Continuity Conditions Between Adjacent Toric Surface Patches
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Sun, Lanyin; Zhu, Chungang; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Toric surface patch is the multi-sided generalization of classical Bézier surface patch. Geometric continuity of the parametric surface patches plays a crucial role in geometric modeling. In this paper, the necessary and sufficient conditions of curvature continuity between toric surface patches are illustrated with the theory of toric degeneration. Furthermore, some practical sufficient conditions of curvature continuity of toric surface patches are also developed.
Non-Local Low-Rank Normal Filtering for Mesh Denoising
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Li, Xianzhi; Zhu, Lei; Fu, Chi-Wing; Heng, Pheng-Ann; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
This paper presents a non-local low-rank normal filtering method for mesh denoising. By exploring the geometric similarity between local surface patches on 3D meshes in the form of normal fields, we devise a low-rank recovery model that filters normal vectors by means of patch groups. In summary, our method has the following key contributions. First, we present the guided normal patch covariance descriptor to analyze the similarity between patches. Second, we pack normal vectors on similar patches into the normal-field patch-group (NPG) matrix for rank analysis. Third, we formulate mesh denoising as a low-rank matrix recovery problem based on the prior that the rank of the NPG matrix is high for raw meshes with noise, but can be significantly reduced for denoised meshes, whose normal vectors across similar patches should be more strongly correlated. Furthermore, we devise an objective function based on an improved truncated 'gamma' norm, and derive an optimization procedure using the alternative direction method of multipliers and iteratively re-weighted least squares techniques.We conducted several experiments to evaluate our method using various 3D models, and compared our results against several state-of-the-art methods. Experimental results show that our method consistently outperforms other methods and better preserves the fine details.
Parallel Multigrid for Nonlinear Cloth Simulation
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wang, Zhendong; Wu, Longhua; Fratarcangeli, Marco; Tang, Min; Wang, Huamin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Accurate high-resolution simulation of cloth is a highly desired computational tool in graphics applications. As singleresolution simulation starts to reach the limit of computational power, we believe the future of cloth simulation is in multi-resolution simulation. In this paper, we explore nonlinearity, adaptive smoothing, and parallelization under a full multigrid (FMG) framework. The foundation of this research is a novel nonlinear FMG method for unstructured meshes. To introduce nonlinearity into FMG, we propose to formulate the smoothing process at each resolution level as the computation of a search direction for the original high-resolution nonlinear optimization problem. We prove that our nonlinear FMG is guaranteed to converge under various conditions and we investigate the improvements to its performance. We present an adaptive smoother which is used to reduce the computational cost in the regions with low residuals already. Compared to normal iterative solvers, our nonlinear FMG method provides faster convergence and better performance for both Newton's method and Projective Dynamics. Our experiment shows our method is efficient, accurate, stable against large time steps, and friendly with GPU parallelization. The performance of the method has a good scalability to the mesh resolution, and the method has good potential to be combined with multi-resolution collision handling for real-time simulation in the future.
Light Optimization for Detail Highlighting
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Gkaravelis, Anastasios; Papaioannou, Georgios; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper we propose an effective technique for the automatic arrangement of spot lights and other luminaires on or near user-provided arbitrary mounting surfaces in order to highlight the geometric details of complex objects. Since potential applications include the lighting design for exhibitions and similar installations, the method takes into account obstructing geometry and potential occlusion from visitors and other non-permanent blocking geometry. Our technique generates the most appropriate position and orientation for light sources based on a local contrast maximization near salient geometric features and a clustering mechanism, producing consistent and view-independent results, with minimal user intervention. We validate our method with realistic test cases including multiple and disjoint exhibits as well as high occlusion scenarios.
Biorthogonal Wavelet Surface Reconstruction Using Partial Integrations
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Ren, Xiaohua; Lyu, Luan; He, Xiaowei; Cao, Wei; Yang, Zhixin; Sheng, Bin; Zhang, Yanci; Wu, Enhua; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We introduce a new biorthogonal wavelet approach to creating a water-tight surface defined by an implicit function, from a finite set of oriented points. Our approach aims at addressing problems with previous wavelet methods which are not resilient to missing or nonuniformly sampled data. To address the problems, our approach has two key elements. First, by applying a three-dimensional partial integration, we derive a new integral formula to compute the wavelet coefficients without requiring the implicit function to be an indicator function. It can be shown that the previously used formula is a special case of our formula when the integrated function is an indicator function. Second, a simple yet general method is proposed to construct smooth wavelets with small support. With our method, a family of wavelets can be constructed with the same support size as previously used wavelets while having one more degree of continuity. Experiments show that our approach can robustly produce results comparable to those produced by the Fourier and Poisson methods, regardless of the input data being noisy, missing or nonuniform. Moreover, our approach does not need to compute global integrals or solve large linear systems.
Dynamic Deep Octree for High-resolution Volumetric Painting in Virtual Reality
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Yeojin; Kim, Byungmoon; Kim, Young J.; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
With virtual reality, digital painting on 2D canvas is now being extended to 3D space. In this paper, we generalize the 2D pixel canvas to a 3D voxel canvas to allow artists to synthesize volumetric color fields. We develop a deep and dynamic octree-based painting and rendering system using both CPU and GPU to take advantage of the characteristics of both processors (CPU for octree modeling and GPU for volume rendering). On the CPU-side, we dynamically adjust an octree and incrementally update the octree to GPU with low latency without compromising the frame rates of the rendering. Our octree is balanced and uses a novel 3-neighbor connectivity for format simplicity and efficient storage, while allowing constant neighbor access time in ray casting. To further reduce the GPU-side 3-neighbor computations, we precompute a culling mask in CPU and upload it to GPU. Finally, we analyze the numerical error-propagation in ray casting through high resolution octree and present a theoretical error bound.
Controlling Stroke Size in Fast Style Transfer with Recurrent Convolutional Neural Network
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Yang, Lingchen; Yang, Lumin; Zhao, Mingbo; Zheng, Youyi; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Controlling stroke size in Fast Style Transfer remains a difficult task. So far, only a few attempts have been made towards it, and they still exhibit several deficiencies regarding efficiency, flexibility, and diversity. In this paper, we aim to tackle these problems and propose a recurrent convolutional neural subnetwork, which we call recurrent stroke-pyramid, to control the stroke size in Fast Style Transfer. Compared to the state-of-the-art methods, our method not only achieves competitive results with much fewer parameters but provides more flexibility and efficiency for generalizing to unseen larger stroke size and being able to produce a wide range of stroke sizes with only one residual unit. We further embed the recurrent stroke-pyramid into the Multi-Styles and the Arbitrary-Style models, achieving both style and stroke-size control in an entirely feed-forward manner with two novel run-time control strategies.
Binocular Tone Mapping with Improved Overall Contrast and Local Details
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Zhang, Zhuming; Hu, Xinghong; Liu, Xueting; Wong, Tien-Tsin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Tone mapping is a commonly used technique that maps the set of colors in high-dynamic-range (HDR) images to another set of colors in low-dynamic-range (LDR) images, to fit the need for print-outs, LCD monitors and projectors. Unfortunately, during the compression of dynamic range, the overall contrast and local details generally cannot be preserved simultaneously. Recently, with the increased use of stereoscopic devices, the notion of binocular tone mapping has been proposed in the existing research study. However, the existing research lacks the binocular perception study and is unable to generate the optimal binocular pair that presents the most visual content. In this paper, we propose a novel perception-based binocular tone mapping method, that can generate an optimal binocular image pair (generating left and right images simultaneously) from an HDR image that presents the most visual content by designing a binocular perception metric. Our method outperforms the existing method in terms of both visual and time performance.
Defocus and Motion Blur Detection with Deep Contextual Features
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Beomseok; Son, Hyeongseok; Park, Seong-Jin; Cho, Sunghyun; Lee, Seungyong; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We propose a novel approach for detecting two kinds of partial blur, defocus and motion blur, by training a deep convolutional neural network. Existing blur detection methods concentrate on designing low-level features, but those features have difficulty in detecting blur in homogeneous regions without enough textures or edges. To handle such regions, we propose a deep encoder-decoder network with long residual skip-connections and multi-scale reconstruction loss functions to exploit high-level contextual features as well as low-level structural features. Another difficulty in partial blur detection is that there are no available datasets with images having both defocus and motion blur together, as most existing approaches concentrate only on either defocus or motion blur. To resolve this issue, we construct a synthetic dataset that consists of complex scenes with both types of blur. Experimental results show that our approach effectively detects and classifies blur, outperforming other state-of-the-art methods. Our method can be used for various applications, such as photo editing, blur magnification, and deblurring.
Few-shot Learning of Homogeneous Human Locomotion Styles
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Mason, Ian; Starke, Sebastian; Zhang, He; Bilen, Hakan; Komura, Taku; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Using neural networks for learning motion controllers from motion capture data is becoming popular due to the natural and smooth motions they can produce, the wide range of movements they can learn and their compactness once they are trained. Despite these advantages, these systems require large amounts of motion capture data for each new character or style of motion to be generated, and systems have to undergo lengthy retraining, and often reengineering, to get acceptable results. This can make the use of these systems impractical for animators and designers and solving this issue is an open and rather unexplored problem in computer graphics. In this paper we propose a transfer learning approach for adapting a learned neural network to characters that move in different styles from those on which the original neural network is trained. Given a pretrained character controller in the form of a Phase-Functioned Neural Network for locomotion, our system can quickly adapt the locomotion to novel styles using only a short motion clip as an example. We introduce a canonical polyadic tensor decomposition to reduce the amount of parameters required for learning from each new style, which both reduces the memory burden at runtime and facilitates learning from smaller quantities of data. We show that our system is suitable for learning stylized motions with few clips of motion data and synthesizing smooth motions in real-time.

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