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"abstract": "Using standard handheld interfaces for VR locomotion may not provide a believable self-motion experience and can contribute to unwanted side effects such as motion sickness, disorientation, or increased cognitive load. This paper demonstrates how using a seated leaning-based locomotion interface –HeadJoystick– in VR ground-based navigation affects user experience, usability, and performance. In three within-subject studies, we compared controller (touchpad/thumbstick) with a more embodied interface (”HeadJoystick”) where users moved their head and/or leaned in the direction of desired locomotion. In both conditions, users sat on a regular office chair and used it to control virtual rotations. In the first study, 24 participants used HeadJoystick versus Controller in three complementary tasks including reach-the-target, follow-the-path, and racing (dynamic obstacle avoidance). In the second study, 18 participants repeatedly used HeadJoystick versus Controller (8 one-minute trials each) in a reach-the-target task. To evaluate potential benefits of different brake mechanisms, in the third study 18 participants were asked to stop within each target area for one second. All three studies consistently showed advantages of HeadJoystick over Controller: we observed improved performance in all tasks, as well as higher user ratings for enjoyment, spatial presence, immersion, vection intensity, usability, ease of learning, ease of use, and rated potential for daily and long-term use, while reducing motion sickness and task load. Overall, our results suggest that leaning-based interfaces such as HeadJoystick provide an interesting and more embodied alternative to handheld interfaces in driving, reach-the-target, and follow-the-path tasks, and potentially a wider range of scenarios.",
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{
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"title": "AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time",
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"givenName": "Hao-Shu",
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"fullName": "Hao-Shu Fang",
"affiliation": "Department of Electrical and Computer Engineering, Shanghai Jiao Tong University, Shanghai, China",
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"fullName": "Jiefeng Li",
"affiliation": "Department of Electrical and Computer Engineering, Shanghai Jiao Tong University, Shanghai, China",
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{
"givenName": "Hongyang",
"surname": "Tang",
"fullName": "Hongyang Tang",
"affiliation": "Department of Electrical and Computer Engineering, Shanghai Jiao Tong University, Shanghai, China",
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"surname": "Xu",
"fullName": "Chao Xu",
"affiliation": "Department of Electrical and Computer Engineering, Shanghai Jiao Tong University, Shanghai, China",
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"fullName": "Haoyi Zhu",
"affiliation": "Department of Electrical and Computer Engineering, Shanghai Jiao Tong University, Shanghai, China",
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"fullName": "Yong-Lu Li",
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"fullName": "Cewu Lu",
"affiliation": "Department of Electrical and Computer Engineering, Shanghai Jiao Tong University, Shanghai, China",
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"abstract": "While numerous deep approaches to the problem of vision-aided localization have been recently proposed, systems operating in the real world will undoubtedly experience novel sensory states previously unseen even under the most prodigious training regimens. We address the localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes. We demonstrate the generalizability of the OEC modules and describe our unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to spatial grids of pixel coordinates. We evaluate our network against state-of-the-art (SoA) VIO, visual odometry (VO), and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset as well as a micro aerial vehicle (MAV) dataset that we collected in the AirSim simulation environment. We demonstrate better than SoA translational localization performance against comparable SoA approaches on our evaluation sequences.",
"abstracts": [
{
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"content": "While numerous deep approaches to the problem of vision-aided localization have been recently proposed, systems operating in the real world will undoubtedly experience novel sensory states previously unseen even under the most prodigious training regimens. We address the localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes. We demonstrate the generalizability of the OEC modules and describe our unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to spatial grids of pixel coordinates. We evaluate our network against state-of-the-art (SoA) VIO, visual odometry (VO), and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset as well as a micro aerial vehicle (MAV) dataset that we collected in the AirSim simulation environment. We demonstrate better than SoA translational localization performance against comparable SoA approaches on our evaluation sequences.",
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"title": "Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery",
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"abstract": "Collective motions of crowds are common in nature and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union, is a fundamental and universal measurement for various crowd systems. By quantifying the topological structures of collective manifolds of crowd, this paper proposes a descriptor of collectiveness and its efficient computation for the crowd and its constituent individuals. The Collective Merging algorithm is then proposed to detect collective motions from random motions. We validate the effectiveness and robustness of the proposed collectiveness on the system of self-driven particles as well as other real crowd systems such as pedestrian crowds and bacteria colony. We compare the collectiveness descriptor with human perception for collective motion and show their high consistency. As a universal descriptor, the proposed crowd collectiveness can be used to compare different crowd systems. It has a wide range of applications, such as detecting collective motions from crowd clutters, monitoring crowd dynamics, and generating maps of collectiveness for crowded scenes. A new Collective Motion Database, which consists of 413 video clips from 62 crowded scenes, is released to the public.",
"abstracts": [
{
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"content": "Collective motions of crowds are common in nature and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union, is a fundamental and universal measurement for various crowd systems. By quantifying the topological structures of collective manifolds of crowd, this paper proposes a descriptor of collectiveness and its efficient computation for the crowd and its constituent individuals. The Collective Merging algorithm is then proposed to detect collective motions from random motions. We validate the effectiveness and robustness of the proposed collectiveness on the system of self-driven particles as well as other real crowd systems such as pedestrian crowds and bacteria colony. We compare the collectiveness descriptor with human perception for collective motion and show their high consistency. As a universal descriptor, the proposed crowd collectiveness can be used to compare different crowd systems. It has a wide range of applications, such as detecting collective motions from crowd clutters, monitoring crowd dynamics, and generating maps of collectiveness for crowded scenes. A new Collective Motion Database, which consists of 413 video clips from 62 crowded scenes, is released to the public.",
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"content": "This paper describes the development and evaluation of an interactive educational program, Tooth Placement and Identification Coach (ToothPIC). The program uses a game-based learning paradigm and 3D visualization techniques to allow first year dentistry and hygiene students to get familiar with dental anatomy. It provides an interactive and stimulating learning tool for acquiring basic dental skills outside of the classroom. Specifically, it uses interactive 3D graphics to teach students to identify, name, number, align, and orient teeth into their proper location in the dental arch. ToothPIC incorporates elements of a game to make learning attractive for the student. In the process, the student learns not only about the 3D features of each tooth but also about the proper placement of the tooth relative to the gingiva and other teeth. ToothPIC has two modules: Module 1 includes 32 permanent upper and lower teeth that are to be identified and placed into the surrounding gingiva. Module 2 only shows the individual teeth, one at a time, to be identified (similar to a “flash-card”). The evaluation results of ToothPIC indicate that students strongly agree that program meets its goals of self-training and self-evaluation, actively involves the students in learning and is a useful supplement to laboratory practices and lectures.",
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"abstract": "The maximum capacity of fractal D2D (device-to-device) social networks with both direct and hierarchical communications is studied in this paper. Specifically, the fractal networks are characterized by the direct social connection and the self-similarity. First, for a fractal D2D social network with direct social communications, it is proved that the maximum capacity is <inline-formula><tex-math notation=\"LaTeX\">Z_$ \\Theta (\\frac{1}{\\sqrt{n\\,\\log n}})$_Z</tex-math> </inline-formula> if a user communicates with one of his/her direct contacts randomly, where <inline-formula><tex-math notation=\"LaTeX\">Z_$ n$_Z</tex-math> </inline-formula> denotes the total number of users in the network, and it can reach up to <inline-formula><tex-math notation=\"LaTeX\">Z_$ \\Theta (\\frac{1}{\\log n})$_Z</tex-math> </inline-formula> if any pair of social contacts with distance <inline-formula><tex-math notation=\"LaTeX\">Z_$ d$_Z</tex-math> </inline-formula> communicate according to the probability in proportion to <inline-formula><tex-math notation=\"LaTeX\">Z_$ d^{-\\beta }$_Z</tex-math> </inline-formula>. Second, since users might get in touch with others without direct social connections through the inter-connected multiple users, the fractal D2D social network with these hierarchical communications is studied as well, and the related capacity is further derived. Our results show that this capacity is mainly affected by the correlation exponent <inline-formula><tex-math notation=\"LaTeX\">Z_$\\epsilon$_Z</tex-math> </inline-formula> of the fractal structure. The capacity is reduced in proportional to <inline-formula><tex-math notation=\"LaTeX\">Z_$ \\frac{1}{{\\log n}}$_Z</tex-math> </inline-formula> if <inline-formula><tex-math notation=\"LaTeX\">Z_$ 2<\\epsilon <3$_Z</tex-math> </inline-formula>, while the reduction coefficient is <inline-formula><tex-math notation=\"LaTeX\">Z_$ \\frac{1}{n}$_Z</tex-math> </inline-formula> if <inline-formula><tex-math notation=\"LaTeX\">Z_$ \\epsilon >3$_Z</tex-math> </inline-formula>.",
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"content": "The maximum capacity of fractal D2D (device-to-device) social networks with both direct and hierarchical communications is studied in this paper. Specifically, the fractal networks are characterized by the direct social connection and the self-similarity. First, for a fractal D2D social network with direct social communications, it is proved that the maximum capacity is <inline-formula><tex-math notation=\"LaTeX\">$ \\Theta (\\frac{1}{\\sqrt{n\\,\\log n}})$</tex-math> <alternatives><mml:math><mml:mrow><mml:mi>Θ</mml:mi><mml:mo>(</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:msqrt><mml:mrow><mml:mi>n</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:msqrt></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"ying-ieq1-2975783.gif\"/></alternatives></inline-formula> if a user communicates with one of his/her direct contacts randomly, where <inline-formula><tex-math notation=\"LaTeX\">$ n$</tex-math> <alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href=\"ying-ieq2-2975783.gif\"/></alternatives></inline-formula> denotes the total number of users in the network, and it can reach up to <inline-formula><tex-math notation=\"LaTeX\">$ \\Theta (\\frac{1}{\\log n})$</tex-math> <alternatives><mml:math><mml:mrow><mml:mi>Θ</mml:mi><mml:mo>(</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"ying-ieq3-2975783.gif\"/></alternatives></inline-formula> if any pair of social contacts with distance <inline-formula><tex-math notation=\"LaTeX\">$ d$</tex-math> <alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href=\"ying-ieq4-2975783.gif\"/></alternatives></inline-formula> communicate according to the probability in proportion to <inline-formula><tex-math notation=\"LaTeX\">$ d^{-\\beta }$</tex-math> <alternatives><mml:math><mml:msup><mml:mi>d</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mi>β</mml:mi></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href=\"ying-ieq5-2975783.gif\"/></alternatives></inline-formula>. Second, since users might get in touch with others without direct social connections through the inter-connected multiple users, the fractal D2D social network with these hierarchical communications is studied as well, and the related capacity is further derived. Our results show that this capacity is mainly affected by the correlation exponent <inline-formula><tex-math notation=\"LaTeX\">$\\epsilon$</tex-math> <alternatives><mml:math><mml:mi>ε</mml:mi></mml:math><inline-graphic xlink:href=\"ying-ieq6-2975783.gif\"/></alternatives></inline-formula> of the fractal structure. The capacity is reduced in proportional to <inline-formula><tex-math notation=\"LaTeX\">$ \\frac{1}{{\\log n}}$</tex-math> <alternatives><mml:math><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:mfrac></mml:math><inline-graphic xlink:href=\"ying-ieq7-2975783.gif\"/></alternatives></inline-formula> if <inline-formula><tex-math notation=\"LaTeX\">$ 2<\\epsilon <3$</tex-math> <alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo><</mml:mo><mml:mi>ε</mml:mi><mml:mo><</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"ying-ieq8-2975783.gif\"/></alternatives></inline-formula>, while the reduction coefficient is <inline-formula><tex-math notation=\"LaTeX\">$ \\frac{1}{n}$</tex-math> <alternatives><mml:math><mml:mfrac><mml:mn>1</mml:mn><mml:mi>n</mml:mi></mml:mfrac></mml:math><inline-graphic xlink:href=\"ying-ieq9-2975783.gif\"/></alternatives></inline-formula> if <inline-formula><tex-math notation=\"LaTeX\">$ \\epsilon >3$</tex-math> <alternatives><mml:math><mml:mrow><mml:mi>ε</mml:mi><mml:mo>></mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"ying-ieq10-2975783.gif\"/></alternatives></inline-formula>.",
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"normalizedAbstract": "The maximum capacity of fractal D2D (device-to-device) social networks with both direct and hierarchical communications is studied in this paper. Specifically, the fractal networks are characterized by the direct social connection and the self-similarity. First, for a fractal D2D social network with direct social communications, it is proved that the maximum capacity is - if a user communicates with one of his/her direct contacts randomly, where - denotes the total number of users in the network, and it can reach up to - if any pair of social contacts with distance - communicate according to the probability in proportion to -. Second, since users might get in touch with others without direct social connections through the inter-connected multiple users, the fractal D2D social network with these hierarchical communications is studied as well, and the related capacity is further derived. Our results show that this capacity is mainly affected by the correlation exponent - of the fractal structure. The capacity is reduced in proportional to - if -, while the reduction coefficient is - if -.",
"title": "On the Capacity of Fractal D2D Social Networks with Hierarchical Communications",
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"authors": [
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"givenName": "Ying",
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"fullName": "Ying Chen",
"affiliation": "College of Information Science and Electronic Engineering (ISEE), Zhejiang University, Hangzhou, Zhejiang, China",
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"givenName": "Rongpeng",
"surname": "Li",
"fullName": "Rongpeng Li",
"affiliation": "College of Information Science and Electronic Engineering (ISEE), Zhejiang University, Hangzhou, Zhejiang, China",
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"givenName": "Zhifeng",
"surname": "Zhao",
"fullName": "Zhifeng Zhao",
"affiliation": "Zhejiang Lab., Hangzhou, Zhejiang, China",
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"givenName": "Honggang",
"surname": "Zhang",
"fullName": "Honggang Zhang",
"affiliation": "College of Information Science and Electronic Engineering (ISEE), Zhejiang University, Hangzhou, Zhejiang, China",
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"issue": {
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"year": "2022",
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"abstract": "An adjustable join (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{Adjoin}$_Z</tex-math></inline-formula>) scheme [4] is a symmetric-key primitive that enables a user to securely outsource his database to a server, and later to issue join queries for a pair of columns. When queries are extended to a list of columns, the <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{3Partition}$_Z</tex-math></inline-formula> security of Adjoin schemes [8] does not capture the expected security. To address this deficiency, we introduce the syntax and security notion of multi-adjustable join (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{M-Adjoin}$_Z</tex-math></inline-formula>) schemes. We propose a new security notion for this purpose, which we refer to as <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3Partition}$_Z</tex-math></inline-formula>. The <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{3Partition}$_Z</tex-math></inline-formula> security of <inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{Adjoin}$_Z</tex-math></inline-formula> extends to the <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3Partition}$_Z</tex-math></inline-formula> security of <inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{M-Adjoin}$_Z</tex-math></inline-formula> in a straightforward way. The gap between <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{3Partition}$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3Partition}$_Z</tex-math></inline-formula> is filled with a sequence <inline-formula><tex-math notation=\"LaTeX\">Z_$\\lbrace \\mathtt{M3P}_{k}\\rbrace _{k\\in \\mathbb {N}}$_Z</tex-math></inline-formula> of security definitions where <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3P}_{1}$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3P}_{\\infty }$_Z</tex-math></inline-formula>, respectively, correspond to <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{3Partition}$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3Partition}$_Z</tex-math></inline-formula>. We propose constructions for achieving both <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3Partition}$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3P}_{k}$_Z</tex-math></inline-formula> security levels. Our <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3Partition}$_Z</tex-math></inline-formula>-secure scheme joins <inline-formula><tex-math notation=\"LaTeX\">Z_$m$_Z</tex-math></inline-formula> columns, each containing <inline-formula><tex-math notation=\"LaTeX\">Z_$n$_Z</tex-math></inline-formula> elements, in time <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {O}(n^{m-1})$_Z</tex-math></inline-formula>. Our <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathtt{M3P}_{k}$_Z</tex-math></inline-formula>-secure scheme uses ideas from secret sharing in its construction and does the job in time <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {O}\\big ((m-1)n^k/k\\big)$_Z</tex-math></inline-formula>. It remains open if this barrier is inherent to the security definitions. Our schemes are substantially more efficient than the previous ones.",
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"content": "An adjustable join (<inline-formula><tex-math notation=\"LaTeX\">$\\text{Adjoin}$</tex-math><alternatives><mml:math><mml:mtext>Adjoin</mml:mtext></mml:math><inline-graphic xlink:href=\"rafiee-ieq1-3022320.gif\"/></alternatives></inline-formula>) scheme [4] is a symmetric-key primitive that enables a user to securely outsource his database to a server, and later to issue join queries for a pair of columns. When queries are extended to a list of columns, the <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq2-3022320.gif\"/></alternatives></inline-formula> security of Adjoin schemes [8] does not capture the expected security. To address this deficiency, we introduce the syntax and security notion of multi-adjustable join (<inline-formula><tex-math notation=\"LaTeX\">$\\text{M-Adjoin}$</tex-math><alternatives><mml:math><mml:mtext>M-Adjoin</mml:mtext></mml:math><inline-graphic xlink:href=\"rafiee-ieq3-3022320.gif\"/></alternatives></inline-formula>) schemes. We propose a new security notion for this purpose, which we refer to as <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq4-3022320.gif\"/></alternatives></inline-formula>. The <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq5-3022320.gif\"/></alternatives></inline-formula> security of <inline-formula><tex-math notation=\"LaTeX\">$\\text{Adjoin}$</tex-math><alternatives><mml:math><mml:mtext>Adjoin</mml:mtext></mml:math><inline-graphic xlink:href=\"rafiee-ieq6-3022320.gif\"/></alternatives></inline-formula> extends to the <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq7-3022320.gif\"/></alternatives></inline-formula> security of <inline-formula><tex-math notation=\"LaTeX\">$\\text{M-Adjoin}$</tex-math><alternatives><mml:math><mml:mtext>M-Adjoin</mml:mtext></mml:math><inline-graphic xlink:href=\"rafiee-ieq8-3022320.gif\"/></alternatives></inline-formula> in a straightforward way. The gap between <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq9-3022320.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq10-3022320.gif\"/></alternatives></inline-formula> is filled with a sequence <inline-formula><tex-math notation=\"LaTeX\">$\\lbrace \\mathtt{M3P}_{k}\\rbrace _{k\\in \\mathbb {N}}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mo>{</mml:mo><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:msub><mml:mi mathvariant=\"monospace\">P</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>}</mml:mo></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>∈</mml:mo><mml:mi mathvariant=\"double-struck\">N</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"rafiee-ieq11-3022320.gif\"/></alternatives></inline-formula> of security definitions where <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3P}_{1}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:msub><mml:mi mathvariant=\"monospace\">P</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq12-3022320.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3P}_{\\infty }$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:msub><mml:mi mathvariant=\"monospace\">P</mml:mi><mml:mi>∞</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq13-3022320.gif\"/></alternatives></inline-formula>, respectively, correspond to <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq14-3022320.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq15-3022320.gif\"/></alternatives></inline-formula>. We propose constructions for achieving both <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq16-3022320.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3P}_{k}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:msub><mml:mi mathvariant=\"monospace\">P</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq17-3022320.gif\"/></alternatives></inline-formula> security levels. Our <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3Partition}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:mi mathvariant=\"monospace\">Partition</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq18-3022320.gif\"/></alternatives></inline-formula>-secure scheme joins <inline-formula><tex-math notation=\"LaTeX\">$m$</tex-math><alternatives><mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href=\"rafiee-ieq19-3022320.gif\"/></alternatives></inline-formula> columns, each containing <inline-formula><tex-math notation=\"LaTeX\">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href=\"rafiee-ieq20-3022320.gif\"/></alternatives></inline-formula> elements, in time <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {O}(n^{m-1})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq21-3022320.gif\"/></alternatives></inline-formula>. Our <inline-formula><tex-math notation=\"LaTeX\">$\\mathtt{M3P}_{k}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">M</mml:mi><mml:mn mathvariant=\"monospace\">3</mml:mn><mml:msub><mml:mi mathvariant=\"monospace\">P</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq22-3022320.gif\"/></alternatives></inline-formula>-secure scheme uses ideas from secret sharing in its construction and does the job in time <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {O}\\big ((m-1)n^k/k\\big)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">O</mml:mi><mml:mfenced separators=\"\" open=\"(\" close=\")\"><mml:mrow><mml:mo>(</mml:mo><mml:mi>m</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow><mml:msup><mml:mi>n</mml:mi><mml:mi>k</mml:mi></mml:msup><mml:mo>/</mml:mo><mml:mi>k</mml:mi></mml:mfenced></mml:mrow></mml:math><inline-graphic xlink:href=\"rafiee-ieq23-3022320.gif\"/></alternatives></inline-formula>. It remains open if this barrier is inherent to the security definitions. Our schemes are substantially more efficient than the previous ones.",
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"normalizedAbstract": "An adjustable join (-) scheme [4] is a symmetric-key primitive that enables a user to securely outsource his database to a server, and later to issue join queries for a pair of columns. When queries are extended to a list of columns, the - security of Adjoin schemes [8] does not capture the expected security. To address this deficiency, we introduce the syntax and security notion of multi-adjustable join (-) schemes. We propose a new security notion for this purpose, which we refer to as -. The - security of - extends to the - security of - in a straightforward way. The gap between - and - is filled with a sequence - of security definitions where - and -, respectively, correspond to - and -. We propose constructions for achieving both - and - security levels. Our --secure scheme joins - columns, each containing - elements, in time -. Our --secure scheme uses ideas from secret sharing in its construction and does the job in time -. It remains open if this barrier is inherent to the security definitions. Our schemes are substantially more efficient than the previous ones.",
"title": "Towards More Secure Constructions of Adjustable Join Schemes",
"normalizedTitle": "Towards More Secure Constructions of Adjustable Join Schemes",
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"Databases",
"Servers",
"Outsourcing",
"Syntactics",
"Encryption",
"Secure Database Outsourcing",
"Symmetric Key Primitive",
"Join Query",
"Monotonicity",
"Non Tranisitivity"
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"givenName": "Shahram",
"surname": "Khazaei",
"fullName": "Shahram Khazaei",
"affiliation": "Mathematical Sciences Department, Sharif University of Technology, Tehran, Iran",
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{
"givenName": "Mojtaba",
"surname": "Rafiee",
"fullName": "Mojtaba Rafiee",
"affiliation": "Mathematical Sciences Department, Sharif University of Technology, Tehran, Iran",
"__typename": "ArticleAuthorType"
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"abstract": "<italic>Background:</italic> In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. <italic>Method:</italic> In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (<italic>CILA</italic>) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. <italic>Results and Conclusions:</italic>Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with <inline-formula><tex-math notation=\"LaTeX\">Z_$77\\%$_Z</tex-math></inline-formula> <inline-formula><tex-math notation=\"LaTeX\">Z_$mAP$_Z</tex-math></inline-formula>, <inline-formula><tex-math notation=\"LaTeX\">Z_$97.5\\%$_Z</tex-math></inline-formula> <inline-formula><tex-math notation=\"LaTeX\">Z_$AP^{50}$_Z</tex-math></inline-formula>, and <inline-formula><tex-math notation=\"LaTeX\">Z_$95.5\\%\\ AP^{75}$_Z</tex-math></inline-formula> segmentation <italic>precision</italic>, and <inline-formula><tex-math notation=\"LaTeX\">Z_$95.38\\%$_Z</tex-math></inline-formula> <italic>accuracy</italic>, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.",
"abstracts": [
{
"abstractType": "Regular",
"content": "<italic>Background:</italic> In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. <italic>Method:</italic> In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (<italic>CILA</italic>) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. <italic>Results and Conclusions:</italic>Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with <inline-formula><tex-math notation=\"LaTeX\">$77\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>77</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"lin-ieq1-3089507.gif\"/></alternatives></inline-formula> <inline-formula><tex-math notation=\"LaTeX\">$mAP$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>m</mml:mi><mml:mi>A</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"lin-ieq2-3089507.gif\"/></alternatives></inline-formula>, <inline-formula><tex-math notation=\"LaTeX\">$97.5\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>97</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"lin-ieq3-3089507.gif\"/></alternatives></inline-formula> <inline-formula><tex-math notation=\"LaTeX\">$AP^{50}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>A</mml:mi><mml:msup><mml:mi>P</mml:mi><mml:mn>50</mml:mn></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"lin-ieq4-3089507.gif\"/></alternatives></inline-formula>, and <inline-formula><tex-math notation=\"LaTeX\">$95.5\\%\\ AP^{75}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>95</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mo>%</mml:mo><mml:mspace width=\"4pt\"/><mml:mi>A</mml:mi><mml:msup><mml:mi>P</mml:mi><mml:mn>75</mml:mn></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"lin-ieq5-3089507.gif\"/></alternatives></inline-formula> segmentation <italic>precision</italic>, and <inline-formula><tex-math notation=\"LaTeX\">$95.38\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>95</mml:mn><mml:mo>.</mml:mo><mml:mn>38</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"lin-ieq6-3089507.gif\"/></alternatives></inline-formula> <italic>accuracy</italic>, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Background: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. Method: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (CILA) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. Results and Conclusions:Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with - -, - -, and - segmentation precision, and - accuracy, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.",
"title": "A Clinical Dataset and Various Baselines for Chromosome Instance Segmentation",
"normalizedTitle": "A Clinical Dataset and Various Baselines for Chromosome Instance Segmentation",
"fno": "09456991",
"hasPdf": true,
"idPrefix": "tb",
"keywords": [
"Cellular Biophysics",
"Diseases",
"Genetics",
"Image Segmentation",
"Learning Artificial Intelligence",
"Medical Image Processing",
"Obstetrics",
"Clinical Dataset Show",
"P 75 Segmentation Precision",
"Current Chromosome Instance Segmentation Methods",
"Chromosome Instance Segmentation Problem",
"Automatic Chromosome Karyotyping Analysis",
"Skilled Clinical Analysts",
"Multiple Segmentation Baselines",
"Overlapping Chromosome Clusters",
"Touching Chromosome Clusters",
"Deep Learning Based Chromosome Instance Segmentation Models",
"Collecting Privacy Removal Chromosome Clusters",
"Annotating 1 Privacy Removal Chromosome Clusters",
"655 Privacy Removal Chromosome Clusters",
"Dataset Augmentation Algorithm",
"Deep Learning Based Models",
"Chromosome Instance Segmentation Framework",
"Biological Cells",
"Image Segmentation",
"Task Analysis",
"Gray Scale",
"Clustering Algorithms",
"Genetics",
"Computer Architecture",
"Chromosome Karyotyping Analysis",
"Chromosome Instance Segmentation",
"Chromosome Segmentation",
"Overlapping And Touching Chromosome Segmentation"
],
"authors": [
{
"givenName": "Runhua",
"surname": "Huang",
"fullName": "Runhua Huang",
"affiliation": "South China Normal University, Guangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Chengchuang",
"surname": "Lin",
"fullName": "Chengchuang Lin",
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"abstract": "To obtain a screening tool for colorectal cancer (CRC) based on gut microbiota, we seek here to identify an optimal classifier for CRC detection as well as a novel nonlinear feature selection method for determining the most discriminative microbial species. In this study, the intestinal microflora in feces of 141 patients were modeled using general regression neural networks (GRNNs) combined with the proposed feature selection method. The proposed model led to slightly higher accuracy <inline-formula><tex-math notation=\"LaTeX\">Z_${\\mathrm{(AUC}}= {\\mathrm{0.911}})$_Z</tex-math></inline-formula> than previous studies <inline-formula><tex-math notation=\"LaTeX\">Z_${\\mathrm{(AUC}}< {\\mathrm{0.87}})$_Z</tex-math></inline-formula>. The results show that the <italic>Clostridium scindens</italic> and <italic>Bifidobacterium angulatum</italic> are indicators of healthy gut flora and CRC happens to reduce these bacterial species. In addition, <italic>Fusobacterium gonidiaformans</italic> was found to be closely correlated with the CRC. The occurrence of colorectal adenoma was not sufficiently discriminatory based on fecal microbiota implicating that the change of colonic flora happens in the advanced phase of CRC development rather than initial adenoma. Integrating the proposed model with fecal occult blood test (FOBT), the CRC detection accuracy remained nearly unchanged <inline-formula><tex-math notation=\"LaTeX\">Z_${\\mathrm{(AUC}}= {\\mathrm{0.915}})$_Z</tex-math></inline-formula>. The performance of the proposed method is validated using independent cohorts from America and Austria. Our results suggest that the proposed feature selection method combined with GRNN is potentially an accurate method for CRC detection.",
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"abstract": "In this article, we present <italic>RoCC</italic>, a robust congestion control approach for datacenter networks based on RDMA. <italic>RoCC</italic> leverages switch queue size as an input to a PI controller, which computes the fair data rate of flows in the queue. The PI parameters are self-tuning to guarantee stability, rapid convergence, and fair and near-optimal throughput in a wide range of congestion scenarios. Our simulation and DPDK implementation results show that <italic>RoCC</italic> can achieve up to <inline-formula><tex-math notation=\"LaTeX\">Z_$7\\times$_Z</tex-math></inline-formula> reduction in PFC frames generated under high load levels, compared to DCQCN. At the same time, <italic>RoCC</italic> can achieve <inline-formula><tex-math notation=\"LaTeX\">Z_$1.7-4.5\\times$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$1.4-3.9\\times$_Z</tex-math></inline-formula> lower tail latency for long flows and <inline-formula><tex-math notation=\"LaTeX\">Z_$2.1-7\\times$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$3.5-8.2\\times$_Z</tex-math></inline-formula> lower tail latency for short flows, compared to DCQCN and HPCC, respectively. We also find that <italic>RoCC</italic> does not require PFC. The functional components of <italic>RoCC</italic> can be efficiently implemented in P4 and FPGA-based switch hardware.",
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"abstract": "Magnetic resonant coupling wireless power transfer (MRC-WPT) enables convenient device-charging. When MIMO MRC-WPT system incorporated with multiple relay components, both relay <italic>On-Off</italic> state (i.e., <italic>power routing</italic>) and TX current (i.e., <italic>current scheduling</italic>) could be adjusted for improving charging efficiency and distance. Previous approaches need the collaboration and feedback from the energy receiver (RX), achieved using side-channels, e.g., Bluetooth, which is time/energy-consuming. In this work we propose, design, and implement a multi-relay MIMO MRC-WPT system, and design an almost optimum joint optimization of <italic>P</italic>ower <italic>RO</italic>uting and <italic>C</italic>urrent <italic>S</italic>cheduling method named <italic>PROCS</italic>, without relying on any feedback from RX. We carefully decompose the joint optimization problem into two subproblems without affecting the overall optimality of the combined solution. For current scheduling subproblem, we propose an almost-optimum RX-feedback independent solution. For power routing subproblem, we first design a greedy algorithm with <inline-formula><tex-math notation=\"LaTeX\">Z_$\\frac{1}{2}$_Z</tex-math></inline-formula> approximation ratio, and then design a DQN based method to further improve its effectiveness. We prototype our system and evaluate it with extensive experiments. Our results demonstrate the effectiveness of the proposed algorithms. The achieved power transfer efficiency (PTE) on average is <inline-formula><tex-math notation=\"LaTeX\">Z_$3.2X$_Z</tex-math></inline-formula>, <inline-formula><tex-math notation=\"LaTeX\">Z_$1.43X$_Z</tex-math></inline-formula>, <inline-formula><tex-math notation=\"LaTeX\">Z_$1.34X$_Z</tex-math></inline-formula>, and <inline-formula><tex-math notation=\"LaTeX\">Z_$7.3X$_Z</tex-math></inline-formula> over the other four strategies: Without relay, with non-adjustable relays, greed based, and shortest-path based ones.",
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"abstract": "Large-scale datasets and deep generative models have enabled impressive progress in human face reenactment. Existing solutions for face reenactment have focused on processing real face images through facial landmarks by generative models. Different from real human faces, artistic human faces (e.g., those in paintings, cartoons, etc.) often involve exaggerated shapes and various textures. Therefore, directly applying existing solutions to artistic faces often fails to preserve the characteristics of the original artistic faces (e.g., face identity and decorative lines along face contours) due to the domain gap between real and artistic faces. To address these issues, we present <italic>ReenactArtFace</italic>, the first effective solution for transferring the poses and expressions from human videos to various artistic face images. We achieve artistic face reenactment in a coarse-to-fine manner. First, we perform <italic>3D artistic face reconstruction</italic>, which reconstructs a textured 3D artistic face through a 3D morphable model (3DMM) and a 2D parsing map from an input artistic image. The 3DMM can not only rig the expressions better than facial landmarks but also render images under different poses/expressions as coarse reenactment results robustly. However, these coarse results suffer from self-occlusions and lack contour lines. Second, we thus perform <italic>artistic face refinement</italic> by using a personalized conditional adversarial generative model (cGAN) fine-tuned on the input artistic image and the coarse reenactment results. For high-quality refinement, we propose a contour loss to supervise the cGAN to faithfully synthesize contour lines. Quantitative and qualitative experiments demonstrate that our method achieves better results than the existing solutions.",
"abstracts": [
{
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"content": "Large-scale datasets and deep generative models have enabled impressive progress in human face reenactment. Existing solutions for face reenactment have focused on processing real face images through facial landmarks by generative models. Different from real human faces, artistic human faces (e.g., those in paintings, cartoons, etc.) often involve exaggerated shapes and various textures. Therefore, directly applying existing solutions to artistic faces often fails to preserve the characteristics of the original artistic faces (e.g., face identity and decorative lines along face contours) due to the domain gap between real and artistic faces. To address these issues, we present <italic>ReenactArtFace</italic>, the first effective solution for transferring the poses and expressions from human videos to various artistic face images. We achieve artistic face reenactment in a coarse-to-fine manner. First, we perform <italic>3D artistic face reconstruction</italic>, which reconstructs a textured 3D artistic face through a 3D morphable model (3DMM) and a 2D parsing map from an input artistic image. The 3DMM can not only rig the expressions better than facial landmarks but also render images under different poses/expressions as coarse reenactment results robustly. However, these coarse results suffer from self-occlusions and lack contour lines. Second, we thus perform <italic>artistic face refinement</italic> by using a personalized conditional adversarial generative model (cGAN) fine-tuned on the input artistic image and the coarse reenactment results. For high-quality refinement, we propose a contour loss to supervise the cGAN to faithfully synthesize contour lines. Quantitative and qualitative experiments demonstrate that our method achieves better results than the existing solutions.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Large-scale datasets and deep generative models have enabled impressive progress in human face reenactment. Existing solutions for face reenactment have focused on processing real face images through facial landmarks by generative models. Different from real human faces, artistic human faces (e.g., those in paintings, cartoons, etc.) often involve exaggerated shapes and various textures. Therefore, directly applying existing solutions to artistic faces often fails to preserve the characteristics of the original artistic faces (e.g., face identity and decorative lines along face contours) due to the domain gap between real and artistic faces. To address these issues, we present ReenactArtFace, the first effective solution for transferring the poses and expressions from human videos to various artistic face images. We achieve artistic face reenactment in a coarse-to-fine manner. First, we perform 3D artistic face reconstruction, which reconstructs a textured 3D artistic face through a 3D morphable model (3DMM) and a 2D parsing map from an input artistic image. The 3DMM can not only rig the expressions better than facial landmarks but also render images under different poses/expressions as coarse reenactment results robustly. However, these coarse results suffer from self-occlusions and lack contour lines. Second, we thus perform artistic face refinement by using a personalized conditional adversarial generative model (cGAN) fine-tuned on the input artistic image and the coarse reenactment results. For high-quality refinement, we propose a contour loss to supervise the cGAN to faithfully synthesize contour lines. Quantitative and qualitative experiments demonstrate that our method achieves better results than the existing solutions.",
"title": "ReenactArtFace: Artistic Face Image Reenactment",
"normalizedTitle": "ReenactArtFace: Artistic Face Image Reenactment",
"fno": "10061279",
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"keywords": [
"Faces",
"Three Dimensional Displays",
"Image Reconstruction",
"Videos",
"Geometry",
"Generators",
"Fitting",
"3 DMM",
"Artistic Faces",
"Face Reenactment",
"Generative Models"
],
"authors": [
{
"givenName": "Linzi",
"surname": "Qu",
"fullName": "Linzi Qu",
"affiliation": "School of Creative Media, City University of Hong Kong, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jiaxiang",
"surname": "Shang",
"fullName": "Jiaxiang Shang",
"affiliation": "Department of Computer Science & Engineering, HKUST, Hong Kong",
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{
"givenName": "Xiaoguang",
"surname": "Han",
"fullName": "Xiaoguang Han",
"affiliation": "Shenzhen Research Institute of Big Data, Chinese University of Hong Kong, Shenzhen, Hong Kong",
"__typename": "ArticleAuthorType"
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{
"givenName": "Hongbo",
"surname": "Fu",
"fullName": "Hongbo Fu",
"affiliation": "School of Creative Media, City University of Hong Kong, Hong Kong",
"__typename": "ArticleAuthorType"
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