PG2018 Short Papers and Posters

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Extreme Feature Regions for Image Matching
(The Eurographics Association, 2018) Fan, Baijiang; Rao, Yunbo; Pu, Jiansu; Deng, Jianhua; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Extreme feature regions are increasingly critical for many image matching applications on affine image-pairs. In this paper, we focus on the time-consumption and accuracy of using extreme feature regions to do the affine-invariant image matching. Specifically, we proposed novel image matching algorithm using three types of critical points in Morse theory to calculate precise extreme feature regions. Furthermore, Random Sample Consensus (RANSAC) method is used to eliminate the features of complex background, and improve the accuracy of the extreme feature regions. Moreover, the saddle regions is used to calculate the covariance matrix for image matching. Extensive experiments on several benchmark image matching databases validate the superiority of the proposed approaches over many recently proposed affine-invariant SIFT algorithms.
GPU-based Real-time Cloth Simulation for Virtual Try-on
(The Eurographics Association, 2018) Su, Tongkui; Zhang, Yan; Zhou, Yu; Yu, Yao; Du, Sidan; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present a novel real-time approach for dynamic detailed clothing simulation on a moving body. The most distinctive feature of our method is that it divides dynamic simulation into two parts: local driving and static cloth simulation. In local driving, feature points of clothing will be handled between two consecutive frames. And then we apply static cloth simulation for a specific frame. Both parts are ecxuted in an entire parallel way. In practice, our system achieves real-time virtual try-on using a depth camera to capture the moving body model and meanwhile, keeps high-fidelity. Experimental results indicate that our method has significant speedups over prior related techniques.
Facial-Expression-Aware Emotional Color Transfer Based On Convolutional Neural Network
(The Eurographics Association, 2018) Pei, Min; Liu, Shiguang; Zhang, Xiaoli; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Emotional color transfer aims to change the evoked emotion of the source image to that of the target image by adjusting color distribution. Most of existing emotional color transfer methods ignore the facial expression features in the image. Therefore, we propose a new facial-expression-aware emotional color transfer framework. We firstly predict the emotion label of the image through the emotion classification network. Then, emotion labels are matched with pre-trained emotional models. Finally, we use the matched emotion model to transfer the color of the target image to the input image. Experiments demonstrate that our method outperforms the state-of-the-arts, which can successfully capture and transfer sophisticated emotion features.
Japanese Kanji Font Style Transfer based on GAN with Unpaired Training
(The Eurographics Association, 2018) Sakai, Hiroki; Niino, Daisuke; Ijiri, Takashi; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
To design a whole package of Japanese font is labor consuming, since it usually contains about 30k kanji characters. To support an efficient design process, this poster attempts to adopt a style transfer algorithm for font package completion. Given two font packages where one contains all characters and the other lacks a large part, we train CycleGAN to perform style transfer between the two packages and transfer the style from the former to the latter. To illustrate the feasibility of our technique, we performed style transfer experiments and achieved visually plausible results by using a relatively small training data set.
Gauss-Seidel Progressive Iterative Approximation (GS-PIA) for Loop Surface Interpolation
(The Eurographics Association, 2018) Wang, Zhihao; Li, Yajuan; Ma, Weiyin; Deng, Chongyang; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We propose a Gauss-Seidel progressive iterative approximation (GS-PIA) method for Loop subdivision surface interpolation by combining classical Gauss-Seidel iterative method for linear system and progressive iterative approximation (PIA) for data interpolation. We prove that GS-PIA is convergent by applying matrix theory. GS-PIA algorithm retains the good features of the classical PIA method, such as the resemblance with the given mesh and the advantages of both a local method and a global method. Compared with some existed interpolation methods of subdivision surfaces, GS-PIA algorithm has advantages in three aspects. First, it has a faster convergence rate compared with the PIA and WPIA algorithms. Second, compared with WPIA algorithm, GS-PIA algorithm need not to choose weights. Third, GS-PIA need not to modify the mesh topology compared with other methods with fairness measures. Numerical examples for Loop subdivision surfaces interpolation illustrated in this paper show the efficiency and effectiveness of GS-PIA algorithm.
TAVE: Template-based Augmentation of Visual Effects to Human Actions in Videos
(The Eurographics Association, 2018) Liu, Jingyuan; Zhou, Xuren; Fu, Hongbo; Tai, Chiew-Lan; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present TAVE, a framework that allows novice users to add interesting visual effects by mimicking human actions in a given template video, in which pre-defined visual effects have already been associated with specific human actions. Our framework is mainly based on high-level features of human pose extracted from video frames, and uses low-level image features as the auxiliary information. We encode an action into a set of code sequences representing joint motion directions and use a finite state machine to recognize the action state of interest. The visual effects, possibly with occlusion masks, can be automatically transferred from the template video to a target video containing similar human actions.
Robust Material Graphs for Volume Rendering
(The Eurographics Association, 2018) Sharma, Ojaswa; Arora, Tushar; Khattar, Apoorv; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
A good transfer function in volume rendering requires careful consideration of the materials present in a volume. In this work we propose a graph based method that considerably reduces manual effort required in designing a transfer function and provides an easy interface for interaction with the volume. Our novel contribution is in proposing an algorithm for robust deduction of a material graph from a set of disconnected edges. Since we compute material topology of the objects, an enhanced rendering is possible with our method. This also allows us to selectively render objects and depict adjacent materials in a volume.
Tabby: Explorable Design for 3D Printing Textures
(The Eurographics Association, 2018) Suzuki, Ryo; Yatani, Koji; Gross, Mark D.; Yeh, Tom; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
This paper presents Tabby, an interactive and explorable design tool for 3D printing textures. Tabby allows texture design with direct manipulation in the following workflow: 1) select a target surface, 2) sketch and manipulate a texture with 2D drawings, and then 3) generate 3D printing textures onto an arbitrary curved surface. To enable efficient texture creation, Tabby leverages an auto-completion approach which automates the tedious, repetitive process of applying texture, while allowing flexible customization. Our user evaluation study with seven participants confirms that Tabby can effectively support the design exploration of different patterns for both novice and experienced users.
Effects of Surface Anisotropy on Perception of Car Body Attractiveness
(The Eurographics Association, 2018) Filip, Jiri; Kolafová, Martina; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In the automotive industry effect coatings are used to introduce customized product design, visually communicating the unique impression of a car. Industrial effect coatings systems achieve primarily a globally isotropic appearance, i.e., surface appearance that does not change when material rotates around its normal. To the contrary, anisotropic appearance exhibits variable behavior due to oriented structural elements. This paper studies to what extent anisotropic appearance improves a visual impression of a car body beyond a standard isotropic one. We ran several psychophysical studies identifying the proper alignment of an anisotropic axis over a car body, showing that regardless of the illumination conditions, subjects always preferred an anisotropy axis orthogonal to car body orientation. The majority of subjects also found the anisotropic appearance more visually appealing than the isotropic one.
A Deep Learned Method for Video Indexing and Retrieval
(The Eurographics Association, 2018) Men, Xin; Zhou, Feng; Li, Xiaoyong; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper, we proposed a deep neural network based method for content based video retrieval. Our approach leveraged the deep neural network to generate the semantic information and introduced the graph-based storage structure to establish the video indices. We devised the Inception-Single Shot Multibox Detector (ISSD) and RI3D model to extract spatial semantic information (objects) and extract temporal semantic information (actions). Our ISSD model achieved a mAP of 26.7% on MS COCO dataset, increasing 3.2% over the original SSD model, while the RI3D model achieved a top-1 accuracy of 97.7% on dataset UCF-101. And we also introduced the graph structure to build the video index with the temporal and spatial semantic information. Our experiment results showed that the deep learned semantic information is highly effective for video indexing and retrieval.
Light-Field DVR on GPU for Streaming Time-Varying Data
(The Eurographics Association, 2018) Ganter, David; Alain, Martin; Hardman, David; Smolic, Aljosa; Manzke, Michael; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Direct Volume Rendering (DVR) of volume data can be a memory intensive task in terms of footprint and cache-coherency. Rayguided methods may not be the best option to interactively render to light-fields due to feedback loops and sporadic sampling, and pre-computation can rule out time-varying data. We present a pipelined approach to schedule the rendering of sub-regions of streaming time-varying volume data while minimising intermediate sub-buffers needed, sharing the work load between CPU and GPU. We show there is significant advantage to using such an approach.
Bottom-up/Top-down Geometric Object Reconstruction with CNN Classification for Mobile Education
(The Eurographics Association, 2018) Guo, Ting; Cui, Rundong; Qin, Xiaoran; Wang, Yongtao; Tang, Zhi; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Geometric objects in educational materials are often illustrated as 2D line drawings, which results in the loss of depth information. To alleviate the problem of fully understanding the 3D structure of geometric objects, we propose a novel method to reconstruct the 3D shape of a geometric object illustrated in a line drawing image. In contrast to most existing methods, ours directly take a single line drawing image as input and generate a valid sketch for reconstruction. Given a single input line drawing image, we first classify the geometric object in the image with convolution neural network (CNN). More specifically, we pre-train the model with simulated images to alleviate the problems of data collection and unbalanced distribution among different classes. Then, we generate the sketch of the geometric object with our proposed bottom-up and top-down scheme. Finally, we finish reconstruction by minimizing an objective function of reconstruction error. Extensive experimental results demonstrate that our method performs significantly better in both accuracy and efficiency compared with the existing methods.
3D VAE-Attention Network: A Parallel System for Single-view 3D Reconstruction
(The Eurographics Association, 2018) Hu, Fei; Yang, Xinyan; Zhong, Wei; Ye, Long; Zhang, Qin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
3D object reconstruction from single view image is a challenge task. Due to the fact that the information contained in one isolated image is not sufficient for reasonable 3D shape reconstruction, the existing results on single-view 3D reconstruction always lack marginal voxels. To tackle this problem, we propose a parallel system named 3D VAE-attention network (3VAN) for single view 3D reconstruction. Distinct from the common encoder-decoder structure, the proposed network consists of two parallel branches, 3D-VAE and Attention Network. 3D-VAE completes the general shape reconstruction by an extension of standard VAE model, and Attention Network supplements the missing details by a 3D reconstruction attention network. In the experiments, we verify the feasibility of our 3VAN on the ShapeNet and PASCAL 3D+ datasets. By comparing with the state-of-art methods, the proposed 3VAN can produce more precise 3D object models in terms of both qualitative and quantitative evaluation.
InspireMePosing: Learn Pose and Composition from Portrait Examples
(The Eurographics Association, 2018) Sheng, Bin; Jin, Yuxi; Li, Ping; Wang, Wenxiao; Fu, Hongbo; Wu, Enhua; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Since people tend to build relationship with others by personal photography, capturing high quality photographs on mobile device has become a strong demand. We propose a portrait photography guidance system to guide user's photographing. We consider current scene image as our input and find professional photograph examples with similar aesthetic features for it. Deep residual network is introduced to gather scene classification information and represent common photograph rules by features, and random forest is adopted to establishing mapping relations between extracted features and examples. Besides, we implement our guidance system on a camera application and evaluate it by user study.
Spherical Blue Noise
(The Eurographics Association, 2018) Wong, Kin-Ming; Wong, Tien-Tsin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present a physically based method which generates unstructured uniform point set directly on the S2-sphere. Spherical uniform point sets are useful for illumination sampling in Quasi Monte Carlo (QMC) rendering but it is challenging to generate high quality uniform point sets directly. Most methods rely on mapping the low discrepancy unit square point sets to the spherical domain. However, these transformed point sets often exhibit sub-optimal uniformity due to the inability of preserving the low discrepancy properties. Our method is designed specifically for direct generation of uniform point sets in the spherical domain. We name our generated result as Spherical Blue Noise point set because it shares similar point distribution characteristics with the 2D blue noise. Our point sets possess high spatial uniformity without a global structure, and we show that they deliver competitive results for illumination integration in QMC rendering, and general numerical integration on the spherical domain.
Shape Interpolation via Multiple Curves
(The Eurographics Association, 2018) Sahillioglu, Yusuf; Aydinlilar, Melike; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present a method that interpolates new shapes between a given pair of source and target shapes. To this end, we utilize a database of related shapes that is used to replace the direct transition from the source to the target by a composition of small transitions. This so-called data-driven interpolation scheme proved useful as long as the database is sufficiently large. We advance this idea one step further by processing the database shapes part by part, which in turn enables realistic interpolations with relatively small databases. We obtain promising preliminary results and point out potential improvements that we intend to address in our future work.
Mesh Parameterization: a Viewpoint from Constant Mean Curvature Surfaces
(The Eurographics Association, 2018) Zhao, Hui; Su, Kehua; Li, Chenchen; Zhang, Boyu; Liu, Shirao; Yang, Lei; Lei, Na; Gortler, Steven J.; Gu, Xianfeng; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present a unified mesh paramterization algorithm for both planar and spheric domains based on mesh deformation. Unlike previous methods, our approach can produce intermediate frames from the original to target meshes. We derive and define a novel geometric flow: unit normal flow(UNF) and prove that if unit normal flow converges, it will deform a surface to a constant mean curvature(CMC) surface, such as plane and sphere. Our method works by deforming meshes of disk topology to planes, meshes of spheric topology to spheres. The unit normal flow we propose also suggests a potential direction for creating CMC surfaces.
Skeleton-based Generalized Cylinder Deformation under the Relative Curvature Condition
(The Eurographics Association, 2018) Ma, Ruibin; Zhao, Qingyu; Wang, Rui; Damon, James; Rosenman, Julian; Pizer, Stephen; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Deformation of a generalized cylinder with a parameterized shape change of its centerline is a non-trivial task when the surface is represented as a high-resolution triangle mesh, particularly when self-intersection and local distortion are to be avoided. We introduce a deformation approach that satisfies these properties based on the skeleton (densely sampled centerline and cross sections) of a generalized cylinder. Our approach uses the relative curvature condition to extract a reasonable centerline for a generalized cylinder whose orthogonal cross sections will not intersect. Given the desired centerline shape as a parametric curve, the displacements on the cross sections are determined while controlling for twisting effects, and under this constraint a vertex-wise displacement field is calculated by minimizing a quadratic surface bending energy. The method is tested on complicated generalized cylindrical objects. In particular, we discuss one application of the method for human colon (large intestine) visualization.
Anisotropic Spectral Manifold Wavelet Descriptor for Deformable Shape Analysis and Matching
(The Eurographics Association, 2018) Li, Qinsong; Liu, Shengjun; Hu, Ling; Liu, Xinru; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper, we present a novel framework termed Anisotropic Spectral Manifold Wavelet Transform (ASMWT) for shape analysis. ASMWT comprehensively analyzes the signals from multiple directions on local manifold regions of the shape with a series of low-pass and band-pass frequency filters in each direction. Using the ASMWT coefficients of a very simple function, we efficiently construct a localizable and discriminative multiscale point descriptor, named as the Anisotropic Spectral Manifold Wavelet Descriptor (ASMWD). Since the filters used in our descriptor are direction-sensitive and able to robustly reconstruct the signals with a finite number of scales, it makes our descriptor be intrinsic-symmetry unambiguous, compact as well as efficient. The extensive experimental results demonstrate that our method achieves significant performance than several state-of-the-art methods when applied in vertex-wise shape matching.
Robust and Efficient SPH Simulation for High-speed Fluids with the Dynamic Particle Partitioning Method
(The Eurographics Association, 2018) Zheng, Zhong; Gao, Yang; Li, Shuai; Qin, Hong; Hao, Aimin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper, our research efforts are devoted to the efficiency issue of the SPH simulation when the ratio of velocities among fluid particles is large. Specifically, we introduce a k-means clustering method into the SPH framework to dynamically partition fluid particles into two disjoint groups based on their velocities, we then use a two-scale time step scheme for these two types of particles. The smaller time steps are for particles with higher speed in order to preserve temporal details and guarantee the numerical stability. In contrast, the larger time steps are used for particles with smaller speeds to reduce the computational expense, and both types of particles are tightly coupled in the simulation.We conduct various experiments which have manifested the advantages of our methods over the conventional SPH technique and its new variants in terms of efficiency and stability.

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