PG2018 Short Papers and Posters

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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.
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.
Direct Limit Volumes: Constant-Time Limit Evaluation for Catmull-Clark Solids
(The Eurographics Association, 2018) Altenhofen, Christian; Müller, Joel; Weber, Daniel; Stork, André; Fellner, Dieter W.; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present a novel approach for efficient limit volume evaluation on Catmull-Clark (CC) subdivision solids. Although several analogies exist between subdivision surfaces and subdivision volumes, extending Stam's limit evaluation technique from 2 to 3 dimensions is not straightforward, as irregularities and boundaries introduce new challenges in the volumetric case. We present new direct evaluation techniques for irregular volumetric topologies and boundary cells, which allow for calculating the limit of CC subdivision solids at arbitrary parameter values in constant time. Evaluation of limit points is a central aspect when using CC solids for applications such as simulation and multi-material additive manufacturing, or as a compact volumetric representation scheme for continuous scalar fields. We demonstrate that our approach is faster than existing evaluation techniques for every topological configuration or target parameter (u, v, w) that requires more than two local subdivision steps.
Progressive 3D Scene Understanding with Stacked Neural Networks
(The Eurographics Association, 2018) Song, Youcheng; Sun, Zhengxing; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
3D scene understanding is difficult due to the natural hierarchical structures and complicated contextual relationships in the 3d scenes. In this paper, a progressive 3D scene understanding method is proposed. The scene understanding task is decomposed into several different but related tasks, and semantic objects are progressively separated from coarse to fine. It is achieved by stacking multiple segmentation networks. The former network segments the 3D scene at a coarser level and passes the result as context to the latter one for a finer-grained segmentation. For the network training, we build a connection graph (vertices indicating objects and edges' weights indicating contact area between objects), and calculate a maximum spanning tree to generate coarse-to-fine labels. Then we train the stacked network by hierarchical supervision based on the generated coarseto- fine labels. Finally, using the trained model, we can not only obtain better segmentation accuracy at the finest-grained than directly using the segmentation network, but also obtain a hierarchical understanding result of the 3d scene as a bonus.
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.
Frontmatter: Pacific Graphics 2018 - Short Papers and Posters
(The Eurographics Association, 2018) Fu, Hongbo; Ghosh, Abhijeet; Kopf, Johannes; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
StretchDenoise: Parametric Curve Reconstruction with Guarantees by Separating Connectivity from Residual Uncertainty of Samples
(The Eurographics Association, 2018) Ohrhallinger, Stefan; Wimmer, Michael; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We reconstruct a closed denoised curve from an unstructured and highly noisy 2D point cloud. Our proposed method uses a two-pass approach: Previously recovered manifold connectivity is used for ordering noisy samples along this manifold and express these as residuals in order to enable parametric denoising. This separates recovering low-frequency features from denoising high frequencies, which avoids over-smoothing. The noise probability density functions (PDFs) at samples are either taken from sensor noise models or from estimates of the connectivity recovered in the first pass. The output curve balances the signed distances (inside/outside) to the samples. Additionally, the angles between edges of the polygon representing the connectivity become minimized in the least-square sense. The movement of the polygon's vertices is restricted to their noise extent, i.e., a cut-off distance corresponding to a maximum variance of the PDFs. We approximate the resulting optimization model, which consists of higher-order functions, by a linear model with good correspondence. Our algorithm is parameter-free and operates fast on the local neighborhoods determined by the connectivity. This enables us to guarantee stochastic error bounds for sampled curves corrupted by noise, e.g., silhouettes from sensed data, and we improve on the reconstruction error from ground truth. Source code is available online. An extended version is available at: https://arxiv.org/abs/1808.07778
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.
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.
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.
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.
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.
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.
A Visual Analytics Approach for Traffic Flow Prediction Ensembles
(The Eurographics Association, 2018) Kong, Kezhi; Ma, Yuxin; Ye, Chentao; Lu, Junhua; Chen, Xiqun; Zhang, Wei; Chen, Wei; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Traffic flow prediction plays a significant role in Intelligent Transportation Systems (ITS). Due to the variety of prediction models, the prediction results form an intricate structure of ensembles and hence leave a challenge of understanding and evaluating the ensembles from different perspectives. In this paper, we propose a novel visual analytics approach for analyzing the predicted ensembles. Our approach models the uncertainty of different traffic flow prediction results. The variations of space, time, and network structures of those results are presented with the visualization designs. The visual interface provides a suite of interactions to enhance exploration of the ensembles. With the system, analysts can discover some intrinsic patterns in the ensemble. We use real-world urban traffic data to demonstrate the effectiveness of our system.
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.

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