Bas-relief is characterized by its unique presentation of intrinsic shape properties and/or detailed appearance using materials raised up in different degrees above a background. However, many bas-relief modeling methods could not manipulate scene details well. We propose a simple and effective solution for two kinds of bas-relief modeling (i.e., structure-preserving and detail-preserving) which is different from the prior tone mapping alike methods. Our idea originates from an observation on typical 3D models, which are decomposed into a piecewise smooth base layer and a detail layer in normal field. Proper manipulation of the two layers contributes to both structure-preserving and detail-preserving bas-relief modeling. We solve the modeling problem in a discrete geometry processing setup that uses normal-based mesh processing as a theoretical foundation. Specifically, using the two-step mesh smoothing mechanism as a bridge, we transfer the bas-relief modeling problem into a discrete space, and solve it in a least-squares manner. Experiments and comparisons to other methods show that (i) geometry details are better preserved in the scenario with high compression ratios, and (ii) structures are clearly preserved without shape distortion and interference from details.

Most graphics hardware features memory to store textures and vertex data for rendering. However, because of the irreversible trend of increasing complexity of scenes, rendering a scene can easily reach the limit of memory resources. Thus, vertex data are preferably compressed, with a requirement that they can be decompressed during rendering. In this paper, we present a novel method to exploit existing hardware texture compression circuits to facilitate the decompression of vertex data in graphics processing unit (GPUs). This built-in hardware allows real-time, random-order decoding of data. However, vertex data must be packed into textures, and careless packing arrangements can easily disrupt data coherence. Hence, we propose an optimization approach for the best vertex data permutation that minimizes compression error. All of these result in fast and high-quality vertex data decompression for real-time rendering. To further improve the visual quality, we introduce vertex clustering to reduce the dynamic range of data during quantization. Our experiments demonstrate the effectiveness of our method for various vertex data of 3D models during rendering with the advantages of a minimized memory footprint and high frame rate.

It is usual for a consumer to search a product based on its category and go to related kind of shop to buy a product, e.g. food in supermarket, a pencil from a stationary shop and etc. While it is not uncommon nowadays for a shop to sell various categories of goods at the same time, like a newspaper stand do sell toys, an accessory shop has stationary. However, consumer may not easily notice and purchase these goods, especially if they are in hurry or not familiar with the shops nearby. With the emergence and popularity of many shopping search engines (shopbots), we can actually provide a better matching between the consumer and seller. In this paper, we developed a shopbot app system (Where2Buy) on smartphone that can search and filter the nearby shops which sell the desired products. To simplify the input process, our system allows users to search by text or voice and fuzzy matching is supported to widen the scope of searching. Detailed information of related product and shops are displayed in the result, together with a navigation map showing the best route to the target shops. If the desired product is not available nearby, substitutes in the same category will be recommended for the users. Our user study evidences that our system is simply and easy to use. More than half of the participants prefer Where2Buy than the other available shopbots in Hong Kong.

The recent advance in deep learning technologies has provided many opportunities to the industries in developing smarter products, such as smart toys, smart cars and smart homes. Unfortunately, a common practical issue to these deep learning methods is the high requirements in computing power even for the prediction part. As the deep learning models' complexities increase, the memory requirements for these models usually exceed the limit of many low end computing devices, such as mobile IoT devices. It is even worse when a multimodel approach is employed. In this paper, we will demonstrate with the development of multimodal emotion analysis on a smart toy, where the user's speech, facial expression and action are used for the understanding of user's emotion. By trimming down DNN complexity or replaced by other learning approaches, we are able to sequeeze four classifiers in 800MB of memory. Finally, results of these methods are ensembled with a fusion approach using a fully connected neural network to obtain a more accuracy and stable result. Our multimodal approach achieved an improvement of about 20% when comparing any unimodal emotion analysis.

In 2015, around 3,000 websites, including Google, Yahoo, Facebook, Youtube and Twitter were blocked in mainland China under the policy of Internet censorship. Quite a number of these websites are social media and instant messaging related. This motivates us to investigate about the relationship between online activities and offline activities. We are especially interested in whether online political participation do promote offline political participation. In this study, we examine the relationship between the login frequency to social media, online involvement of political participation and offline involvement of political participation of Facebook users. A total of 107 adult were invited in this study by completing survey. Typically, we believe that there is a close relationship between one's acts during offline period and his/her online behaviors. However, our results showed that there is no causal correlation between online duration of Facebook and offline involvement of political participation.

Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices. The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise. Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface. In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS). This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features. The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting. Our framework is direct, practical, and easy to understand. Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression.

In this paper, we proposed a serious game that not only for entertainment but also encourage people to do more exercise, in order to let them relax and refresh from the stressful working life and maintain a good health and well-being. Our game applied motion capture, muscle power sensing, action game factors and virtual reality technology (VR). In contrast to traditional VR games, our game provides a novel muscle-power sensitive gaming interface, such that special effects will be shown and a higher bonus scores could be received when the player do exercise harder and exert a greater muscle power. The preliminary experiment result reveals that the muscle sensing capability facilitates the players to pay more attention to their muscle activities when playing the game. The players also agreed that the proposed game could attract them to do exercise again.

Spin image is a powerful shape descriptor, useful in a point set or surface registration. However, the usage of spin images is hampered by issues such as sensitivity to noise and sampling rate and time-consuming matching process. We propose a novel spin-image-based local surface descriptor named spin contour to alleviate these problems. This descriptor is not an image but a 2-D point set. Comparisons show that the spin contour is robust to noise and sampling differences. The matching time is also improved over spin images.

Most mesh denoising techniques utilize only either the facet normal field or the vertex normal field of a mesh surface. The two normal fields, though contain some redundant geometry information of the same model, can provide additional information that the other field lacks. Thus, considering only one normal field is likely to overlook some geometric features. In this paper, we take advantage of the piecewise consistent property of the two normal fields and propose an effective framework in which they are filtered and integrated using a novel method to guide the denoising process. Our key observation is that, decomposing the inconsistent field at challenging regions into multiple piecewise consistent fields makes the two fields complementary to each other and produces better results. Our approach consists of three steps: vertex classification, bi-normal filtering, and vertex position update. The classification step allows us to filter the two fields on a piecewise smooth surface rather than a surface that is smooth everywhere. Based on the piecewise consistence of the two normal fields, we filtered them using a piecewise smooth region clustering strategy. To benefit from the bi-normal filtering, we design a quadratic optimization algorithm for vertex position update. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than current approaches on surfaces with multifarious geometric features and irregular surface sampling.

Online shopping systems nowadays are constructed with the capability of displaying text and 2D images to the customers. Although it is enough for the purpose of exchanging product information between sellers and buyers, the plain and dull shopping experience is not really appreciated. With the recent advancement of 3D technologies in the web environment, we can enhance such an experience with 3D visualized products. However, the creation of 3D content is a major challenge in realizing a 3D enabled shopping system. The high cost of 3D scanning devices inhibits the popularity of scanned 3D objects on the web and related applications. Therefore, one major targets of our system is to provide a low cost 3D scanning solution suitable for naïve web users. We exploit the Kinect device, which is a low-cost and fast depth sensor with color, in the development of a 3D scanning system. With an integration of a marker-based tracking system to estimate the current view angle of Kinect sensor during depth acquisition, the obtained depth data can reconstruct the 3D model by a preliminary coordinate frame alignment and a registration of all point clouds. The whole process of capturing, storing, uploading, compressing and displaying 3D models of products on web application is done with minimal user involvements. The introduction of attractive and fashionable 3D product on the web will gain significant attention from customers and evolve habits and traditions in e-commerce, especially for C2C business, in which sellers can promote their goods for sale with scanned 3D models.

This paper presents a novel approach to simulating turbulent flows by developing an adaptive multirelaxation scheme in the framework of lattice Boltzmann equation (LBE). Existing LBE methods in graphics simulations are usually insufficient for turbulent flows since the collision term disturbs the underlying stability and accuracy. We adopt LBE with the multiple relaxation time (MRT) collision model (MRT-LBE), and address this issue by enhancing the collision-term modeling. First, we employ renormalization group analysis and formulate a new turbulence model with an adaptive correction method to compute more appropriate eddy viscosities on a uniform lattice structure. Efficient algebraic calculations are retained with small-scale turbulence details while maintaining the system stability. Second, we note that for MRT-LBE, predicting single eddy viscosity per lattice node may still result in instability. Hence, we simultaneously predict multiple eddy viscosities for stress-tensor-related elements, thereby asynchronously computing multiple relaxation parameters to further enhance the MRT-LBE stability. With these two new strategies, turbulent flows can be simulated with finer visual details even on coarse grid configurations. We demonstrate our results by simulating and visualizing various turbulent flows, particularly with smoke animations, where stable turbulent flows with high Reynolds numbers can be faithfully produced.

Abstract Turbulence modeling has recently drawn many attentions in fluid animation to generate small‐scale rolling features. Being one of the widely adopted approaches, vorticity confinement method re‐injects lost energy dissipation back to the flow. However, previous works suffer from deficiency when large vorticity coefficientεis used, due to the fact that constantεis applied all over the simulated domain. In this paper, we propose a novel approach to enhance the visual effect by employing an adaptive vorticity confinement which varies the strength with respect to the helicity instead of a user‐defined constant. To further improve fine details in turbulent flows, we are not only applying our proposed vorticity confinement to low‐resolution grid, but also on a finer grid to generate sub‐grid level turbulence. Since the incompressible Navier–Stokes equations are solved only in low‐resolution grid, this saves a significant amount of computation. Several experiments demonstrate that our method can produce realistic smoke animation with enhanced turbulence effects in real‐time. Copyright © 2011 John Wiley & Sons, Ltd.

Color and texture are basic elements in digital graphics. Selection of color with a picker is convenient in many of the image editing softwares. However, more organized and intelligent GUI for texture pattern selection is still missing. In this paper, we attempt to fill this gap with the introduction of several robust techniques in building an intuitive texture picking GUI.

There are many successful experiences employing texture analysis to improve the accuracy and robustness of image segmentation. Usually, per-pixel based texture analysis is required, which involves intensive computation especially for large images. Precomputation and storing of the texture features involves large file space which is not cost effective. To adopt to these novel needs, we propose the use of multidimensional scaling (MDS) technique to reduce the size of per-pixel texture features of an image while preserving the textural discrminiability for segmentation. Per-pixel texture features will create very large dissimilarity matrix, making the solving of MDS intractable. A sampling-based MDS is therefore introduced to tackle the problem with a divide-and-conquer approach. A compression ratio of 1:24 can be achieved with an average error rate lower than 7%. Preliminary experiments on segmentation using the compressed data show satisfactory results as good as using the uncompressed features. We foresee that such a method will allow texture features to be stored and transferred more efficiently on low processing power devices or embedded systems like mobile phones.

Realistic facial animation is important in many graphics applications, like animated feature films and computer games, to enrich human computer interaction. In this paper, we propose a physically-based facial animation approach employing knowledge from the anatomy and biomechanics of human facial muscles. First, the 3D face mesh is generated automatically by commercial software and the facial skin is represented by a nonlinear mass-spring system which simulates realistic elastic dynamics of human dermis. Then, a structure skull is attached to fit the face mesh. A set of anatomically consistent facial muscles are incorporated to model the forces deforming the face mesh. Finally, we extend Waters' muscle model to improve the combination of multiple muscle actions and to generate realistic expression. Experiments show that our method is superior to the traditional geometric model and can achieve comparative results with the commercial software FaceGen.

Pseudo-random number generators (PRNG) have been intensively used in many stochastic algorithms in artificial intelligence, computer graphics and other scientific computing. However, the current commodity GPU design does not facilitate the efficient implementation of high-quality PRNGs that require high-precision integer arithmetics and bitwise operations. In this paper, we propose a framework to generate a high-quality PRNG shader for all kinds of GPUs. We adopt the cellular automata (CA) PRNG to facilitate high speed and parallel random number generation. The configuration of the CA PRNG is completed automatically by optimizing an objective function that accounts for quality of generated random sequences. To visually evaluate the result, we apply the best PRNG shader to photon mapping. Timing statistics show that our GPU parallelized PRNG is much faster than a pure CPU implementation.

This paper presents an optimization-based halftoning technique that preserves the structure and tone similarities between the original and the halftone images. By optimizing an objective function consisting of both the structure and the tone metrics, the generated halftone images preserve visually sensitive texture details as well as the local tone. It possesses the blue-noise property and does not introduce annoying patterns. Unlike the existing edge-enhancement halftoning, the proposed method does not suffer from the deficiencies of edge detector. Our method is tested on various types of images. In multiple experiments and the user study, our method consistently obtains the best scores among all tested methods.

Due to the tediousness and labor intensive cost, some manga artists have already employed computer-assisted methods for converting color photographs to manga backgrounds. However, existing bitonal image generation methods usually produce unsatisfactory uniform screening results that are not consistent with traditional mangas, in which the artist employs a rich set of screens. In this paper, we propose a novel method for generating bitonal manga backgrounds from color photographs. Our goal is to preserve the visual richness in the original photograph by utilizing not only screen density, but also the variety of screen patterns. To achieve the goal, we select screens for different regions in order to preserve the tone similarity, texture similarity, and chromaticity distinguishability. The multi-dimensional scaling technique is employed in such a color-to-pattern matching for maintaining pattern dissimilarity of the screens. Users can control the mapping by a few parameters and interactively fine-tune the result. Several results are presented to demonstrate the effectiveness and convenience of the proposed method.

This paper presents a modular framework to efficiently apply the bidirectional texture functions (BTF) onto object surfaces. The basic building blocks are the BTF tiles. By constructing one set of BTF tiles, a wide variety of objects can be textured seamlessly without re-synthesizing the BTF. The proposed framework nicely decouples the surface appearance from the geometry. With this appearance-geometry decoupling, one can build a library of BTF tile sets to instantaneously dress and render various objects under variable lighting and viewing conditions. The core of our framework is a novel method for synthesizing seamless high-dimensional BTF tiles, that are difficult for existing synthesis techniques. Its key is to shorten the cutting paths and broaden the choices of samples so as to increase the chance of synthesizing seamless BTF tiles. To tackle the enormous data, the tile synthesis process is performed in compressed domain. This not just allows the handling of large BTF data during the synthesis, but also facilitates compact storage of the BTF in GPU memory during the rendering.