We present a method for prediction of a person's hairstyle from a single image. Despite growing use cases in user digitization and enrollment for virtual experiences, available methods are limited, particularly in the range of hairstyles they can capture. Human hair is extremely diverse and lacks any universally accepted description or categorization, making this a challenging task. Most current methods rely on parametric models of hair at a strand level. These approaches, while very promising, are not yet able to represent short, frizzy, coily hair and gathered hairstyles. We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system. Previous classification approaches have been restricted by poorly labeled data that lacks diversity, imposing constraints on the usefulness of any resulting enrollment system. We use only synthetic data to train our models. This allows for explicit control of diversity of hairstyle attributes, hair colors, facial appearance, poses, environments and other parameters. It also produces noise-free ground-truth labels. We introduce a novel hairstyle taxonomy developed in collaboration with a diverse group of domain experts which we use to balance our training data, supervise our model, and directly measure fairness. We annotate our synthetic training data and a real evaluation dataset using this taxonomy and release both to enable comparison of future hairstyle prediction approaches. We employ an architecture based on a pre-trained feature extraction network in order to improve generalization of our method to real data and predict taxonomy attributes as an auxiliary task to improve accuracy. Results show our method to be significantly more robust for challenging hairstyles than recent parametric approaches.

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

While the Fat-Tree network topology represents the dominant state-of-art solution for large-scale HPC networks, its scalability in terms of power, latency, complexity, and cost is significantly challenged by the ever-increasing communication bandwidth among tens of thousands of heterogeneous computing nodes. We propose 3D-Hyper-FleX-LION, a flat hybrid electronic-photonic interconnect network that leverages the multichannel nature of modern multi-terabit switch ASICs (with 100 Gb/s granularity) and a reconfigurable all-to-all photonic fabric called Flex-LIONS. Compared to a Fat-Tree network interconnecting the same number of nodes and with the same oversubscription ratio, the proposed 3D-Hyper-FleX-LION offers a 20% smaller diameter, 3x lower power consumption, 10X fewer cable connections, and 4x reduction in the number of transceivers. When bandwidth reconfiguration capabilities of Flex-LIONS are exploited for non-uniform traffic workloads, simulation results indicate that 3D-Hyper-FleX-LION can achieve up to 4x improvement in energy efficiency for synthetic traffic workloads with high locality compared to Fat-Tree.

Silicon Photonics (SiPs) can overcome the energy and bandwidth limitations of electrical interconnects in networks-on-chip (NoCs) and enable efficient global all-to-all connectivity-the ideal from a performance perspective. Unfortunately, state-of-the-art SiP switching fabrics impose power overheads for thermo-optical control of microring resonators (MRs), excessive crosstalk, or challenging physical layout. The Arrayed Waveguide Grating Router (AWGR) is a SiP device that provides layout-efficient and scalable all-to-all connectivity through wavelength-routing on a passive and compact platform. Recent technological advances now enable AWGR integration with significantly reduced footprint (2), crosstalk (<;-38dB), and loss (<;2dB), making AWGRs emerge as a major enabler for energy-efficient all-to-all connectivity in NoCs. This paper, for the first time, populates the design space of AWGR-based NoC topologies and compares AWGRs to state-of-the-art SiP fabrics and aggressive electrical baselines. Our results make a compelling case for AWGRs in interposer-based systems with processor disintegration, which have high bandwidth demands, large on-chip distances, and less stringent area constraints. AWGR-based NoCs can offer an average of 1.67× power savings, 1.23× speed-up, and 1.5× lower energy-delay-product (EDP) on PARSEC3.0/SPLASH-2× workloads compared to electrical baselines, and fewer waveguides, wavelengths, or MRs than alternative SiP crossbar fabrics.

Landmark search is crucial to improve the quality of travel experience. Smart phones make it possible to search landmarks anytime and anywhere. Most of the existing work computes image features on smart phones locally after taking a landmark image. Compared with sending original image to the remote server, sending computed features saves network bandwidth and consequently makes sending process fast. However, this scheme would be restricted by the limitations of phone battery power and computational ability. In this paper, we propose to send compressed (low resolution) images to remote server instead of computing image features locally for landmark recognition and search. To this end, a robust 3D model based method is proposed to recognize query images with corresponding landmarks. Using the proposed method, images with low resolution can be recognized accurately, even though images only contain a small part of the landmark or are taken under various conditions of lighting, zoom, occlusions and different viewpoints. In order to provide an attractive landmark search result, a 3D texture model is generated to respond to a landmark query. The proposed search approach, which opens up a new direction, starts from a 2D compressed image query input and ends with a 3D model search result.

Landmark image classification is a challenging task due to the various circumstances, e.g., illumination, viewpoint, zoom in/out and occlusion under which landmark images are taken. Most existing approaches utilize features extracted from the whole image including both landmark and non-landmark areas. However, non-landmark areas introduce redundant and noisy information. In this paper, we propose a novel approach to improve landmark image classification consisting of three steps. First, an attention-based 3-D reconstruction method is proposed to reconstruct sparse 3-D landmark models. Second, the sparse 3-D models are projected onto iconic images in order to identify images of the hot regions. For a landmark, hot regions are parts of a landmark which attract photographers' attention and are popularly captured in photos. These hot region images are later used to enhance reconstructed sparse 3-D models. Third, the landmark regions are obtained through mapping the enhanced 3-D models to landmark images. A k-dimensional tree (kd-tree) is then constructed for each landmark based on scale invariant feature transform (SIFT) features extracted from the landmark area to classify unlabeled images into pre-defined landmark categories. The proposed method is evaluated using 291 661 images of 51 landmarks. Experiments of comparison indicate that our method outperforms bag-of-words (BoW) based approach 18.5% and method of spatial-pyramid-matching using sparse-coding (ScSPM) 8.4%.

3D reconstruction has been widely used in many important applications. While extensive research has been done in 3D reconstruction, several key issues are still open and the precision of the recovered regions is still far from satisfaction. In this paper, we propose a novel approach to selecting regions of interest in video frames by analyzing multiple spatio-temporal characteristics and reconstructing 3D objects based on the selected regions. Firstly, the static, location and motion attention are extracted from video frames to generate saliency maps. Then, all the video frames are clustered and a candidate set of key frames is extracted based on the saliency maps, where the key frames are extracted according to the constraints in terms of geometry and visibility. Finally, the 3D structure of the attention region is recovered using the selected key frames and the generated saliency maps. The experiments on real-world indoor and outdoor scenes demonstrate that the proposed approach is both more accurate (better attention regions) and computationally more efficient.

Most of the existing approaches for landmark image classification utilize either holistic features or interest of points in the whole image to train the classification model, which may lead to unsatisfactory result due to involvement of much information non-located on the landmark in the training process. In this paper, we propose a novel approach to improve landmark image classification result via a process of 2D to 3D reconstruction and 3D to 2D projection of iconic landmark images. Particularly, we first select iconic images from labeled landmark image collections to reconstruct a 3D landmark represented in point clouds. Then, 3D point clouds are projected back onto the same iconic images to obtain the landmark-region of each iconic image and subsequently extract SIFT features from the landmark-region to construct a k-dimensional tree (kd-tree) for each landmark. This process is able to filter out noise points corresponding to clutter background and non-landmark objects in the iconic images. Finally, the unlabeled images can be classified into predefined landmark categories based on the amount of matched feature points between the image features and the kd-trees. The experimental result and comparison with the state-of-the-art demonstrate the effectiveness of our approach.

Skinning existing animation frames or examples is exploited actively to find the most suitable scheme that is capable of capturing deformations from given mesh sequences. It is necessary to approximate joint transformations by a fitting algorithm that usually involves solving a large scale linear system. In this paper, we reduce the dimensions of the linear system substantially by representing example meshes as progressive decimated models. These models are reconstructed from augmented deformation sensitive decimation (ADSD) that is able to handle large deformations while maintaining connectivity relationship throughout all example meshes. We also show that skinning can be propagated to decimated models, which allows animations to be scalable to specific applications with varied requirements of qualities.

This paper presents a method to extract skeletons using examples. Our method is based on the observation that many deformations in real world applications are isometric or near isometric. By taking advantage of the intrinsic property of harmonic 1-form, i.e., it is determined by the metric and independent of the resolution and embedding, our method can easily find a consistent mapping between the reference and example poses which can be in different resolutions and triangulations. We first construct the skeleton-like Reeb graph of a harmonic function defined on the given poses. Then by examining the changes of mean curvatures, we identify the initial locations of joints. Finally we refine the joint locations by solving a constrained optimization problem. To demonstrate the efficacy of our method, we apply the extracted skeletons to pose space deformation and skeleton transfer.

Abstract Human crowd is a fascinating social phenomenon in nature. This paper presents our work on designing behavior model for virtual humans in a crowd simulation under normal‐life and emergency situations. Our model adopts an agent‐based approach and employs a layered framework to reflect the natural pattern of human‐like decision making process, which generally involves a person's awareness of the situation and consequent changes on the internal attributes. The social group and crowd‐related behaviors are modeled according to the findings and theories observed from social psychology (e.g., social attachment theory). By integrating our model into an agent execution process, each individual agent can response differently to the perceived environment and make realistic behavioral decisions based on various physiological, emotional, and social group attributes. To demonstrate the effectiveness of our model, a case study has been conducted, which shows that realistic human behaviors can be generated at both individual and group level. Copyright © 2008 John Wiley & Sons, Ltd.