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{"paper": "3046704403", "venue": "2626360753", "year": "2019", "title": "visualization with three js", "label": ["36464697", "121684516", "21442007"], "author": ["3046652139"], "reference": [], "abstract": "", "title_raw": "Visualization with Three.js", "abstract_raw": "", "match_count": 1, "cited_by_count": 0}
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{"paper": "3092592429", "venue": "2626360753", "year": "2020", "title": "omniphotos casual 360 vr photography", "label": ["109950114", "2778852477", "205711294", "194969405", "44185422", "54988407", "31972630", "15759828"], "author": ["2885922908", "3091880608", "3092647312", "2965892617"], "reference": ["1586856071", "1606751047", "2080900088", "2091685917", "2104925593", "2112947410", "2118428504", "2133665775", "2140950877", "2171282290", "2295628903", "2471962767", "2472178346", "2474439225", "2517799340", "2520525508", "2531849700", "2554759989", "2560474170", "2561270043", "2605221279", "2738549586", "2766456119", "2770128544", "2770684966", "2783870313", "2810330571", "2811153284", "2896892492", "2901982540", "2902486335", "2903233845", "2916495731", "2921751438", "2932489395", "2943445277", "2949657144", "2949825757", "2962785568", "2963557767", "2964288609", "2964496817", "2967423693", "2968257580", "2969485315", "2971278627", "2982574419", "2999450559", "3011969621", "3022900804", "3033011849", "3034528136", "3034728336", "3034801905", "3035524985", "3043139608", "3048402306", "3084311597", "3103957575", "3108325989", "3109585842"], "abstract": "", "title_raw": "OmniPhotos: Casual 360\u00b0 VR Photography", "abstract_raw": "", "match_count": 0}
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{"paper": "2770392468", "venue": "2626360753", "year": "2017", "title": "sketch based implicit blending", "label": ["162597733", "16963264", "11413529", "76449508", "77660652"], "author": ["2223823413", "1986355708", "1990602849", "2046411192", "1966882323"], "reference": ["193111080", "810216307", "1519789732", "1963931352", "1977982787", "1983019655", "1986737903", "1989519540", "1992055379", "2006461528", "2026687385", "2039594111", "2069728322", "2071661704", "2084424473", "2088875936", "2092072871", "2096044049", "2097332464", "2098466221", "2103197357", "2104217614", "2104388113", "2106248292", "2106320166", "2116533649", "2121934905", "2129498063", "2142523981", "2146968309", "2154396405", "2161190057", "2161431717", "2166983756", "2179723638", "2244686166", "2484246561", "2552247836", "2558708291", "2619168597"], "abstract": "implicit models can be combined by using composition operators functions that determine the resulting shape recently gradient based composition operators have been used to express a variety of behaviours including smooth transitions sharp edges contact surfaces bulging or any combinations the problem for designers is that building new operators is a complex task that requires specialized technical knowledge in this work we introduce an automatic method for deriving a gradient based implicit operator from 2d drawings that prototype the intended visual behaviour to solve this inverse problem in which a shape defines a function we introduce a general template for implicit operators a user s sketch is interpreted as samples in the 3d operator s domain we fit the template to the samples with a non rigid registration approach the process works at interactive rates and can accommodate successive refinements by the user the final result can be applied to 3d surfaces as well as to 2d shapes our method is able to replicate the effect of any blending operator presented in the literature as well as generating new ones such as non commutative operators we demonstrate the usability of our method with examples in font design collision response modeling implicit skinning and complex shape design", "title_raw": "Sketch-based implicit blending", "abstract_raw": "Implicit models can be combined by using composition operators; functions that determine the resulting shape. Recently, gradient-based composition operators have been used to express a variety of behaviours including smooth transitions, sharp edges, contact surfaces, bulging, or any combinations. The problem for designers is that building new operators is a complex task that requires specialized technical knowledge. In this work, we introduce an automatic method for deriving a gradient-based implicit operator from 2D drawings that prototype the intended visual behaviour. To solve this inverse problem, in which a shape defines a function, we introduce a general template for implicit operators. A user's sketch is interpreted as samples in the 3D operator's domain. We fit the template to the samples with a non-rigid registration approach. The process works at interactive rates and can accommodate successive refinements by the user. The final result can be applied to 3D surfaces as well as to 2D shapes. Our method is able to replicate the effect of any blending operator presented in the literature, as well as generating new ones such as non-commutative operators. We demonstrate the usability of our method with examples in font-design, collision-response modeling, implicit skinning, and complex shape design.", "match_count": 1, "cited_by_count": 44}
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{"paper": "2782015459", "venue": "2626360753", "year": "2015", "title": "a hand gesture control framework on smart glasses", "label": ["138673069", "64729616", "159437735", "207347870", "31972630"], "author": ["2781542172", "2781998035", "280681944", "2589569159", "1182732830", "1936510448"], "reference": [], "abstract": "in this paper we proposed a hand gesture control framework on smart glasses three different camera structures were presented to detect the hand portion and the moore s neighbor tracing algorithm detects the hand contour more efficiently and automatically we not only refined the skin color model but also improved the chamfer matching method for the robust and effective gesture recognition a demonstration has been implemented by using the hand gesture control framework several gestures are pre defined for various functions such as selecting a virtual 3d object rotating zooming in or zooming out and changing display properties of the 3d object", "title_raw": "A hand gesture control framework on smart glasses", "abstract_raw": "In this paper, we proposed a hand gesture control framework on\r\nsmart glasses. Three different camera structures were presented to\r\ndetect the hand portion, and the Moore\u2019s Neighbor tracing\r\nalgorithm detects the hand contour more efficiently and\r\nautomatically. We not only refined the skin-color model but also\r\nimproved the Chamfer matching method for the robust and\r\neffective gesture recognition. \r\nA demonstration has been implemented by using the hand gesture\r\ncontrol framework. Several gestures are pre-defined for various\r\nfunctions, such as selecting a virtual 3D object, rotating, zooming\r\nin or zooming out, and changing display properties of the 3D\r\nobject.", "match_count": 2, "cited_by_count": 3, "cited_by_count_v2": 2}
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{"paper": "2986708244", "venue": "2626360753", "year": "2019", "title": "neural state machine for character scene interactions", "label": ["50644808", "167822520", "48007421", "90697248", "31972630", "108583219", "136389625", "2776214188"], "author": ["2222396038", "2806156347", "2204493835", "2224831486"], "reference": ["1735317348", "1968976745", "1985473518", "1990932462", "2012154540", "2025915818", "2027247754", "2028098496", "2034795687", "2042093148", "2042408133", "2050059500", "2053886812", "2067502459", "2091715447", "2108495500", "2115136127", "2124609748", "2144231991", "2150884987", "2151857564", "2176412452", "2249800031", "2460299708", "2469134594", "2475210682", "2611706523", "2739330054", "2769052358", "2784449021", "2792764867", "2801567109", "2894766094", "2895138769", "2902757548", "2902791462", "2963121255", "2963202339", "3104515094", "3105609823"], "abstract": "we propose neural state machine a novel data driven framework to guide characters to achieve goal driven actions with precise scene interactions even a seemingly simple task such as sitting on a chair is notoriously hard to model with supervised learning this difficulty is because such a task involves complex planning with periodic and non periodic motions reacting to the scene geometry to precisely position and orient the character our proposed deep auto regressive framework enables modeling of multi modal scene interaction behaviors purely from data given high level instructions such as the goal location and the action to be launched there our system computes a series of movements and transitions to reach the goal in the desired state to allow characters to adapt to a wide range of geometry such as different shapes of furniture and obstacles we incorporate an efficient data augmentation scheme to randomly switch the 3d geometry while maintaining the context of the original motion to increase the precision to reach the goal during runtime we introduce a control scheme that combines egocentric inference and goal centric inference we demonstrate the versatility of our model with various scene interaction tasks such as sitting on a chair avoiding obstacles opening and entering through a door and picking and carrying objects generated in real time just from a single model", "title_raw": "Neural state machine for character-scene interactions", "abstract_raw": "We propose Neural State Machine, a novel data-driven framework to guide characters to achieve goal-driven actions with precise scene interactions. Even a seemingly simple task such as sitting on a chair is notoriously hard to model with supervised learning. This difficulty is because such a task involves complex planning with periodic and non-periodic motions reacting to the scene geometry to precisely position and orient the character. Our proposed deep auto-regressive framework enables modeling of multi-modal scene interaction behaviors purely from data. Given high-level instructions such as the goal location and the action to be launched there, our system computes a series of movements and transitions to reach the goal in the desired state. To allow characters to adapt to a wide range of geometry such as different shapes of furniture and obstacles, we incorporate an efficient data augmentation scheme to randomly switch the 3D geometry while maintaining the context of the original motion. To increase the precision to reach the goal during runtime, we introduce a control scheme that combines egocentric inference and goal-centric inference. We demonstrate the versatility of our model with various scene interaction tasks such as sitting on a chair, avoiding obstacles, opening and entering through a door, and picking and carrying objects generated in real-time just from a single model.", "match_count": 1, "cited_by_count": 247}
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{"paper": "2770331385", "venue": "2626360753", "year": "2017", "title": "online generative model personalization for hand tracking", "label": ["119857082", "167966045", "183003079", "48007421", "95020103", "31972630", "2777904410", "126042441"], "author": ["2345250235", "1966882323", "2769943492", "2125943848", "2106076158"], "reference": ["49679257", "1588539311", "1702419847", "1923747199", "1930710394", "1952857803", "1973757125", "1990947293", "1992187125", "1995713470", "2004068758", "2012048984", "2016456873", "2023633446", "2062712751", "2069741261", "2075156252", "2079512211", "2099940712", "2149615975", "2161604086", "2214145768", "2218414108", "2221659194", "2281196926", "2299913536", "2322290486", "2423984454", "2469784314", "2471370490", "2552247836", "2595142274", "2737644856", "2781010022", "2963272646", "3014485129", "3022404379", "3029645440", "3136489197"], "abstract": "we present a new algorithm for real time hand tracking on commodity depth sensing devices our method does not require a user specific calibration session but rather learns the geometry as the user performs live in front of the camera thus enabling seamless virtual interaction at the consumer level the key novelty in our approach is an online optimization algorithm that jointly estimates pose and shape in each frame and determines the uncertainty in such estimates this knowledge allows the algorithm to integrate per frame estimates over time and build a personalized geometric model of the captured user our approach can easily be integrated in state of the art continuous generative motion tracking software we provide a detailed evaluation that shows how our approach achieves accurate motion tracking for real time applications while significantly simplifying the workflow of accurate hand performance capture we also provide quantitative evaluation datasets at http gfx uvic ca datasets handy", "title_raw": "Online generative model personalization for hand tracking", "abstract_raw": "We present a new algorithm for real-time hand tracking on commodity depth-sensing devices. Our method does not require a user-specific calibration session, but rather learns the geometry as the user performs live in front of the camera, thus enabling seamless virtual interaction at the consumer level. The key novelty in our approach is an online optimization algorithm that jointly estimates pose and shape in each frame, and determines the uncertainty in such estimates. This knowledge allows the algorithm to integrate per-frame estimates over time, and build a personalized geometric model of the captured user. Our approach can easily be integrated in state-of-the-art continuous generative motion tracking software. We provide a detailed evaluation that shows how our approach achieves accurate motion tracking for real-time applications, while significantly simplifying the workflow of accurate hand performance capture. We also provide quantitative evaluation datasets at http://gfx.uvic.ca/datasets/handy", "match_count": 1, "cited_by_count": 94}
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{"paper": "2902739662", "venue": "2626360753", "year": "2019", "title": "desia a general framework for designing interlocking assemblies", "label": ["153083717", "120314980", "49585438", "146380142", "144745244", "77660652"], "author": ["2479872435", "2167189457", "2125943848"], "reference": ["563490225", "847326694", "1226829582", "1506516411", "1521252151", "1546210756", "1967203322", "1967724669", "1969196471", "1976379134", "1987419791", "2003031215", "2010328065", "2011032066", "2016352584", "2018964608", "2022635558", "2036777717", "2052638748", "2059040485", "2060508964", "2078469680", "2091632568", "2094755132", "2110662634", "2118382442", "2119370533", "2121223684", "2123250142", "2124877068", "2148207804", "2151140238", "2163875859", "2178846008", "2282975356", "2461498410", "2553710993", "2555223629", "2566430184", "2598655613", "2763315705", "2769298213", "2791339172", "2912493178", "2963835697", "2997885238", "3136868698", "3138982726"], "abstract": "interlocking assemblies have a long history in the design of puzzles furniture architecture and other complex geometric structures the key defining property of interlocking assemblies is that all component parts are immobilized by their geometric arrangement preventing the assembly from falling apart computer graphics research has recently contributed design tools that allow creating new interlocking assemblies however these tools focus on specific kinds of assemblies and explore only a limited space of interlocking configurations which restricts their applicability for design in this paper we propose a new general framework for designing interlocking assemblies the core idea is to represent part relationships with a family of base directional blocking graphs and leverage efficient graph analysis tools to compute an interlocking arrangement of parts this avoids the exponential complexity of brute force search our algorithm iteratively constructs the geometry of assembly components taking advantage of all existing blocking relations for constructing successive parts as a result our approach supports a wider range of assembly forms compared to previous methods and provides significantly more design flexibility we show that our framework facilitates efficient design of complex interlocking assemblies including new solutions that cannot be achieved by state of the art approaches", "title_raw": "DESIA: a general framework for designing interlocking assemblies", "abstract_raw": "Interlocking assemblies have a long history in the design of puzzles, furniture, architecture, and other complex geometric structures. The key defining property of interlocking assemblies is that all component parts are immobilized by their geometric arrangement, preventing the assembly from falling apart. Computer graphics research has recently contributed design tools that allow creating new interlocking assemblies. However, these tools focus on specific kinds of assemblies and explore only a limited space of interlocking configurations, which restricts their applicability for design. In this paper, we propose a new general framework for designing interlocking assemblies. The core idea is to represent part relationships with a family of base Directional Blocking Graphs and leverage efficient graph analysis tools to compute an interlocking arrangement of parts. This avoids the exponential complexity of brute-force search. Our algorithm iteratively constructs the geometry of assembly components, taking advantage of all existing blocking relations for constructing successive parts. As a result, our approach supports a wider range of assembly forms compared to previous methods and provides significantly more design flexibility. We show that our framework facilitates efficient design of complex interlocking assemblies, including new solutions that cannot be achieved by state of the art approaches.", "match_count": 0}
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1184914352 national conference on artificial intelligence National Conference on Artificial Intelligence
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1203999783 international joint conference on artificial intelligence International Joint Conference on Artificial Intelligence
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1158167855 computer vision and pattern recognition Computer Vision and Pattern Recognition
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1124077590 european conference on computer vision European Conference on Computer Vision
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1164975091 international conference on computer vision International Conference on Computer Vision
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1180662882 international conference on machine learning International Conference on Machine Learning
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1130985203 knowledge discovery and data mining Knowledge Discovery and Data Mining
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1127325140 neural information processing systems Neural Information Processing Systems
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1188739475 meeting of the association for computational linguistics Meeting of the Association for Computational Linguistics
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1192655580 empirical methods in natural language processing Empirical Methods in Natural Language Processing
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1173951661 north american chapter of the association for computational linguistics North American Chapter of the Association for Computational Linguistics
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1140684652 international acm sigir conference on research and development in information retrieval International ACM SIGIR Conference on Research and Development in Information Retrieval
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1135342153 the web conference The Web Conference
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| 14 |
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1174091362 architectural support for programming languages and operating systems Architectural Support for Programming Languages and Operating Systems
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1131341566 international symposium on computer architecture International Symposium on Computer Architecture
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| 16 |
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1150919317 international symposium on microarchitecture International Symposium on Microarchitecture
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1126329156 high performance computer architecture High-Performance Computer Architecture
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1152462849 acm special interest group on data communication ACM Special Interest Group on Data Communication
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| 19 |
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1158363782 networked systems design and implementation Networked Systems Design and Implementation
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1198471430 computer and communications security Computer and Communications Security
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1163618098 ieee symposium on security and privacy IEEE Symposium on Security and Privacy
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2537594308 usenix security symposium USENIX Security Symposium
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1180274659 network and distributed system security symposium Network and Distributed System Security Symposium
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1175089206 international conference on management of data International Conference on Management of Data
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101328740 journal of archaeological research Journal of Archaeological Research
|
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