先端技術とメディア表現(FTMA2018) 第2回レポートまとめ

1. '5."
   TVCNJTTJPOT ,FOUB
   :BNBNPUP
2. QVNQQSPCFํࣜͰɼΞϒϨʔγϣϯ౳ʹΑΔද໘ͷมԽΛ ಈతʹ؍ଌ͢ΔγεςϜ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ಈతʹ؍ଌ͢ΔγεςϜ͸͜Ε·Ͱͳ͔ͬͨ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ QVNQQSPCFํࣜͰɼEFMBZͤͨ͞ϨʔβʔΛ౰ͯͳ͕Βಈత ʹ؍ଌ͢Δ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹࡱ૾͠ɼҰఆ࣌ؒޙͷܗঢ়ͱൺֱͯ͠ຊ౰ʹͱΕ͍ͯ Δ͔֬ೝͨ͠ ٞ࿦͸͋Δʁ ಈతʹͱΕΔͱͲΕ͘Β͍خ͍͠ͷͩΖ͏͔ ࣍ʹಡΉ΂͖࿦จ͸ʁ %JSFDU
   GFNUPTFDPOE
   MBTFS
   TVSGBDF
   OBOPNJDSPTUSVDUVSJOH
   BOE
   JUT
   BQQMJDBUJPOT Direct visualization of the complete evolution of femtosecond laser-induced surface structural dynamics of metals (Light: Science Applications 2017, Fang, Ranran and Vorobyev, Anatoliy and Guo, Chunlei) 
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3. ௚઀ϑΣϜτΛ౰ͯͯՃ޻͢Δ࿦จʹؔ͢Δ3FWJFX࿦จ
   
   ද໘ʹφϊϚΠΫϩߏ଄Λ௚઀Ճ޻
   
   ۚଐʹର͢ΔपظతͳϨʔβʔՃ޻ߏ଄
   
   ୯ମͷφϊϗʔϧ΍ΞϨΠঢ়ͷφϊϚΠΫϩϗʔϧ
   
   φϊߏ଄ςΫενϟ͕ࢪ͞ΕͨϚΠΫϩߏ଄
   
   ϑΣϜτϨʔβʔʹΑΔফڈ΍ݚຏ
   
   ΞϓϦέʔγϣϯ ͲΜͳ΋ͷʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ $POUSPMMFE
   OBOPTUSVDUSVSFT
   GPSNBUJPO
   CZ
   VMUSB
   GBTU
   MBTFS
   QVMTFT
   GPS
   DPMPS
   NBSLJOH Direct femtosecond laser surface nano/microstructuring and its applications (Laser Photonics Reviews 2013: Vorobyev, Anatoliy Y and Guo, Chunlei) ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ٞ࿦͸͋Δʁ 
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4. ৭Λூࠁ͢ΔͨΊͷφϊߏ଄Λۚଐද໘ʹϑΣϜτϨʔβʔ Ͱ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ͳͷͰɼίϯτϩʔϧ͞Εͨφϊߏ଄Λۚଐද໘ʹ ϨʔβʔͰߏங͍ͯ͘͠ख๏ͷ৽͠͞ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ φϊߏ଄Λ੍ޚ͢ΔͨΊͷϨʔβʔೖࣹ֯౓ͳͲͷઃܭ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ࡞ͬͯ4&.ࡱ૾ͷ֬ೝʴ࣮෺ͷΧϥʔ֬ೝ ٞ࿦͸͋Δʁ ͳͥଟ͘ͷ৔߹ɼ̎࣠ʹ෼͚ͨޙ$$%ʹೖΕΔޫ࿏Λ࡞Δͷ ͔ʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ -BTFSJOEVDFE
   QFSJPEJD
   TVSGBDF
   TUSVDUVSFTŠB
   TDJFOUJpD
   FWFSHSFFO Controlled nanostructrures formation by ultra fast laser pulses for color marking (Optics express 2010, Dusser, Benjamin and Sagan, Z and Soder, Herv{\'e} and Faure, Nicolas and Colombier, Jean-Philippe and Jourlin, Michel and Audouard, Eric) 
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5. -BTFS*OEFDVEF
   1FSJPEJD
   4VSGBDF
   4USVDUVSFT
   -*144 ʹؔ͢Δ 3FWJFX࿦จ
   
   -4'-ͱ)4'-ͷ̎छྨͷϝΧχζϜ͕͋Δ
   
   ੍ޚʹ͸TJOHMFQVMTF
   TFRVFODF
   EPVCMFQVMTF
   TFRVFODFɼ࣌ ؒ෼ׂճં͕͋Δ
   
   ΞϓϦέʔγϣϯͱͯ͠͸ɼߏ଄৭ɾૄਫੑɾࡉ๔૿৩ɾ τϥΠϘϩδʔʢ५׈ɾຎࡲͳͲʣͳͲ͕ڍ͛ΒΕΔ ͲΜͳ΋ͷʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ (FOFSBUJPO
   PG
   CJPJOTQJSFE
   TUSVDUVSBM
   DPMPST
   WJB
   UXPQIPUPO
   QPMZNFSJ[BUJPO Laser-induced periodic surface structures—a scientific evergreen (IEEE Journal of Selected Topics in Quantum Electronics 2017: Bonse, J{\"o}rn and H{\"o}hm, Sandra and Kirner, Sabrina V and Rosenfeld, Arkadi and Kr{\"u}ger, J{\”o}rg) ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ٞ࿦͸͋Δʁ 
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6. ϞϧϑΥ௏ͷߏ଄ͷΑ͏ͳ΋ͷΛQIPUPO
   QPMZNFSJ[BUJPO
   11 Ͱ࡞੒͢Δ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ϙϦϚʔ࢖ͬͯΔ͔Βɼෳࡶͳखॱͳ͠ʹ̏࣍ݩతʹߏ଄͕ ࡞ΕΔ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ̏࣍ݩతʹ࡞ΔͨΊʹϙϦϚʔ࢖༻͢ΔͷͱɼϨʔβʔՃ޻ ͷख๏ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ࡞ͬͨ΋ͷͷ4&.ͱ৭ݕূ ٞ࿦͸͋Δʁ ϙϦϚʔ࢖ͬͯ̏࣍ݩͷ໰୊ղܾ͸ͲΕ͘Β͍خ͍͠ͷ͔Α ͘Θ͔Μͳ͍ ࣍ʹಡΉ΂͖࿦จ͸ʁ &MFDUSJDBMMZ
   BOE
   FMFDUSPIZESPEZOBNJDBMMZ
   ESJWFO
   QIBTF
   USBOTJUJPO
   BOE
   TUSVDUVSBM
   DPMPS
   TXJUDIJOH
   PG
   PMJHPNFS
   UFUIFSFE
   %
   DPMMPJE Generation of bioinspired structural colors via two-photon polymerization (Scientific reports 2017, Zyla, Gordon and Kovalev, Alexander and Grafen, Markus and Gurevich, Evgeny L and Esen, Cemal and Ostendorf, Andreas and Gorb, Stanislav) 
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7. ిѹͳͲΛม͑Δ͜ͱͰɼ݁থͷ޲͖Λม͑ͯʢӷথతʣɼ ߏ଄৭ͷεΠονϯάΛ࣮ݱ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ BMQIB
   [JSDPOJVN
   QIPTQIBUFΛ࢖ͬͯ៉ྷʹߏ଄৭ͷεΠον ϯάΛ࣮ݱͨ͠఺ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ίϩΠυঢ়ͷBMQIB
   [JSDPOJVN
   QIPTQIBUFͷܗঢ়ͱిՙΛͲ͏ ͔͚Δ͔ͷઃܭ࿦ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ͔͚ΔిѹͳͲΛม͑ͨΓͯ͠ɼ݁ՌΛݟͨΓ ٞ࿦͸͋Δʁ ͲΕ͘Β͍ࣗ༝౓ߴ͍ͷͩΖ͏͔ ࣍ʹಡΉ΂͖࿦จ͸ʁ 1SPHSBNNJOH
   /BOPQBSUJDMFT
   JO
   .VMUJTDBMF
   0QUJDBMMZ
   .PEVMBUFE
   "TTFNCMZ
   BOE
   1IBTF
   4XJUDIJOH
   PG
   4JMJDPO
   /BOPQBSUJDMF
   "SSBZ Electrically and electrohydrodynamically driven phase transition and structural color switching of oligomer tethered 2D colloid (RSC Advances 2018, Masud, Aurangzeb Rashid and Hong, Seung-Ho and Shen, Tian-Zi and Shahzad, Amir and Song, Jang-Kun) 
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8. φϊύʔςΟΫϧͷύλʔϯΛɼύϧεͷΤωϧΪʔ΍มߋ ͳͲʹΑͬͯௐ੔ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ φϊύʔςΟΫϧΛਖ਼֬ʹௐ੔Ͱ͖ͯɼҐ૬΋ม͑ΕΔͱ͜ Ζ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ Ϩʔβʔͷύϧε΍ɼ̎ճ໨Ճ޻ͳͲʢਖ਼֬ʹ͸Θ͔Γ͖ͬ ͯͳ͍ʣ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ࡞ͬͯ4&.ؚΊܭଌ ٞ࿦͸͋Δʁ ੩తʹʁҐ૬ม͑ΕΔͱͲ͏خ͍͠ͷ͔ ࣍ʹಡΉ΂͖࿦จ͸ʁ -BTFS*OEVDFE
   (SBQIFOF
   CZ
   .VMUJQMF
   -BTJOH
   5PXBSE
   &MFDUSPOJDT
   PO
   $MPUI
   1BQFS
   BOE
   'PPE Programming Nanoparticles in Multiscale: Optically Modulated Assembly and Phase Switching of Silicon Nanoparticle Array (ACS nano 2018, Wang, Letian and Rho, Yoonsoo and Shou, Wan and Hong, Sukjoon and Kato, Kimihiko and Eliceiri, Matthew and Shi, Meng and Grigoropoulos, Costas P and Pan, Heng and Carraro, Carlo and others) 
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9. άϥϑΣϯΛ$0ϨʔβʔʹΑͬͯҥ෰΍ࢴɼ৯΂෺ʹࠁҹ ͢Δ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ γϯάϧޫ࿏ͰEFGPDVTϝιουΛ࢖͏͜ͱͰ࣮ݱͨ͠఺ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ EFGPDVTʹ͢Δͱ෺ମ΁ͷ౰ͨΓํ͕มΘΔͷͰɼͦΕΛ༻ ͍Δ͜ͱͰ̍ͭͷޫ࿏ͰͷϨʔβʔՃ޻Λ࣮ݱͨ͠ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ࡞੒͠ɼ4&.ͱ࣮෺ͷ֬ೝ ٞ࿦͸͋Δʁ άϥϑΣϯΛ৯΂෺ʹࠁΊΔͱԿ͕خ͍͠ͷ͔
   ৯΂෺ͷ੍໿͸ͲΕ͘Β͍͋Δͷ͔ ࣍ʹಡΉ΂͖࿦จ͸ʁ )PMPHSBQIJD
   3FTPOBOU
   -BTFS
   1SJOUJOH
   PG
   NFUBTVSGBDFT
   VTJOH
   QMBTNPOJD
   UFNQMBUF Laser-Induced Graphene by Multiple Lasing: Toward Electronics on Cloth, Paper, and Food (ACS nano 2018, Chyan, Yieu and Ye, Ruquan and Li, Yilun and Singh, Swatantra Pratap and Arnusch, Christopher J and Tour, James M) 
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10. ϓϥζϞϯͷςϯϓϨʹϨʔβʔͰϝλද໘Λ࡞Δ͜ͱ
    ϗϩύλʔϯΛ͏ͬͯΔͬΆ͍ʁʢ'SFTOFM
    ;POF
    1MBUFͱͷൺֱ͕Α͘ग़͖ͯ ͨʣ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ 4-.·Ͱ࢖ͬͯ݁ߏޫֶͷηοτΞοϓ͕ॏ͍ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ 4-.࢖ͬͯ؆୯ʹʁճંϨϯζʁΛ࡞͍ͬͯΔ఺ʁ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ࡞ͬͨ΋ͷͷࡱӨ ٞ࿦͸͋Δʁ ͜Ε͕Ͱ͖ΔͱԿ͕خ͍͠ͷ͔Α͘Θ͔Βͳ͔ͬͨ ࣍ʹಡΉ΂͖࿦จ͸ʁ -JHIU*OEVDFE
    5VOJOH
    BOE
    3FDPOpHVSBUJPO
    PG
    /BOPQIPUPOJD
    4USVDUVSFT Holographic Resonant Laser Printing of metasurfaces using plasmonic template (ACS Photonics 2018, Schultz Carstensen, Marcus and Zhu, Xiaolong and Esther Iyore, Oseze and Mortensen, N Asger and Levy, Uriel and Kristensen, Anders) 
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11. φϊϑΥτχοΫߏ଄΁ͷϨʔβʔՃ޻ܥͷ3FWJFX࿦จ
    
    Մٯੑͷ͋ΔมԽΛऑϨʔβʔՃ޻Ͱੜ੒
    
    Մٯੑͷͳ͍มԽΛϨʔβʔՃ޻Ͱੜ੒
    Մٯੑͷ͋ΔՃ޻Λࢪ͢͜ͱ͕Ͱ͖Δ͜ͱʹڻ͖ ͲΜͳ΋ͷʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ )ZESPEZOBNJD
    SPUBUJOH
    NPUJPO
    PG
    NJDSPNPUPST
    GSPN
    GFNUPTFDPOE
    MBTFS
    NJDSPGBCSJDBUJPO Light-Induced Tuning and Reconfiguration of Nanophotonic Structures (Laser Photonics Reviews 2017: Makarov, Sergey V and Zalogina, Anastasia S and Tajik, Mohammad and Zuev, Dmitry A and Rybin, Mikhail V and Kuchmizhak, Aleksandr A and Juodkazis, Saulius and Kivshar, Yuri) ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ٞ࿦͸͋Δʁ 
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12. ϨʔβʔՃ޻ͰϚΠΫϩλʔϏϯΛੜ੒ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ νϧτͷ֯౓΍ϒϨʔυͷ௕͞ɾ਺Λੜ੒࣌ʹௐ੔͢Δ͜ͱ ͕Ͱ͖Δ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ μΠϨΫτϑΣϜτϨʔβʔՃ޻ͰλʔϏϯΛ௚઀࡞ΕΔ
    ͔͠΋ੜ੒ͷύϥϝʔλ΋࣋ͯΔ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ࡞ͬͯɼࡱ૾ͯ͠ɼྲྀମʹྲྀΕΛൃੜͤͯ͞ճΔ͜ͱ ͷݕূ ٞ࿦͸͋Δʁ ̏࣍ݩܗঢ়Λ͜Μͳʹਖ਼֬ʹ࡞ΔͨΊͷ۩ମతͳख๏͸ʁʢઆ ໌͸গͳ͔ͬͨʣ ࣍ʹಡΉ΂͖࿦จ͸ʁ $PNQMFY
    EFXFUUJOH
    TDFOBSJPT
    PG
    VMUSBUIJO
    TJMJDPO
    pMNT
    GPS
    MBSHFTDBMF
    OBOPBSDIJUFDUVSFT Hydrodynamic rotating motion of micromotors from femtosecond laser microfabrication (Sensors and Actuators B: Chemical 2018, Guan, Wei and Lv, Chao and Xu, Yi-Shi and Yu, Yan-Hao and Xia, Hong and Niu, Li-Gang and Liu, Sen and Wang, Gong and Wang, Ying-Shuai and Sun, Hong-Bo) 
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13. ୯݁থͷബ͍ϑΟϧϜͷෳࡶͳEFXFUUJOHΛ׬શʹίϯτ ϩʔϧ͢Δ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ΊͪΌബ͍γϦίϯϑΟϧϜͷEFXFUUJOHΛ׬શʹίϯτϩʔϧ ͯ͠ɼ୯݁থͷφϊߏ଄Λ͍ΖΜͳύλʔϯͰEFXFUUJOHͤͨ͞ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ʢཧղͰ͖·ͤΜͰͨ͠ʣ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ࡞ͬͨ΋ͷͷࡱ૾ٴͼมԽͷܭଌ ٞ࿦͸͋Δʁ ΋ͬͱ΋Α͘Θ͔Βͳ͔ͬͨʢಡΉ࿦จϛεͬͨʣ ࣍ʹಡΉ΂͖࿦จ͸ʁ 0QUJDBM
    'JFME
    &OIBODFNFOU
    JO
    "V
    /BOPQBSUJDMF%FDPSBUFE
    /BOPSPE
    "SSBZT
    1SFQBSFE
    CZ
    'FNUPTFDPOE
    -BTFS
    BOE
    5IFJS
    5VOBCMF
    4&34
    Complex dewetting scenarios of ultrathin silicon films for large-scale nanoarchitectures (Science advances 2017, Naffouti, Meher and Backofen, Rainer and Salvalaglio, Marco and Bottein, Thomas and Lodari, Mario and Voigt, Axel and David, Thomas and Benkouider, Abdelmalek and Fraj, Ibtissem and Favre, Luc and others) 
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14. ۚͷφϊύʔςΟΫϧͷEFDPSBUJPOΛɼϑΣϜτϨʔβʔՃ޻Ͱ४උͨ͠φϊ ϩουΞϨΠʹࢪͨ͠ʢۚφϊϩουͷੜ੒ʣ
    ͦͯ͠ද໘૿ڧϥϚϯࢄཚΛՄมͳ΋ͷʹͨ͠ ͲΜͳ΋ͷʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ϑΣϜτՃ޻ͰφϊϩουΛ࡞ͬͱ͍ͯͦ͜ʹۚΛম͖ͳ· ͠Ͱ෇ணͤ͞Δ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ۭؾதͰγϦίϯʹՃ޻ˠਫதͰ·ͨՃ޻ɼͰφϊϩουΛ ࡞Γɼͦ͜ʹۚͷφϊύʔςΟΫϧΛ͚͍ͭͯ͘ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ࣮ࡍʹ੡଄ϓϩηεΛ௨ͯ͠ɼ4&.ࡱ૾ݕূ ٞ࿦͸͋Δʁ ۚφϊϩουͷΞϓϦέʔγϣϯͷ޿͞͸ ࣍ʹಡΉ΂͖࿦จ͸ʁ 4VQFSIZESPQIPCJD
    BOE
    DPMPSGVM
    DPQQFS
    TVSGBDFT
    GBCSJDBUFE
    CZ
    QJDPTFDPOE
    MBTFS
    JOEVDFE
    QFSJPEJD
    OBOPTUSVDUVSFT Optical Field Enhancement in Au Nanoparticle-Decorated Nanorod Arrays Prepared by Femtosecond Laser and Their Tunable SERS Applications (ACS applied materials interfaces 2017, Cao, Wei and Jiang, Lan and Hu, Jie and Wang, Andong and Li, Xiaowei and Lu, Yongfeng) 
    ࢁຊ݈ଠ
    
    َίʔε
15. FaceShop: Deep Sketch-based Face Image Editing ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ("/εέονϕʔεͰϢʔβʔ͕௚ײతʹը૾ Λฤू͢Δ͜ͱ͕Ͱ͖ΔΑ͏ͳإը૾ฤूͷͨΊ ͷ৽نγεςϜ χϡʔϥϧωοτϫʔΫΛը૾ิ׬ͱը૾ม׵ͷ ೋͭͷಉ࣌λεΫͰ܇࿅ ΠϯλϥΫςΟϒͳը૾ฤूͷͨΊͷϑϨʔϜϫʔ ΫͰˢΛ࠷ॳʹ΍ͬͨ Πϯϓοτը૾ʹϚεΫΛ͔͚ͯϢʔβʔͷೖྗ ৘ใ͕ෆ׬શͰ͋ͬͨͱ͖ͷ࠶ݱΛͯ͠΋ੜ੒ʹ ੒ޭͨ͠ɹࣗಈߏஙͱը૾ิ׬͕ͲͪΒ΋ߴ඼࣭ কདྷతʹ͸ΑΓߴղ૾౓͔ͭଟ༷ͳσʔληοτ Ͱ܇࿅Λߦ͍͍ͨ Siggraph’18 'UNB
    
16. 
 ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ
    σΟʔϓχϡʔϥϧωοτϫʔΫΛ࢖༻ͯ͠ೖྗ ҆੩͔ΒͦΕʹಉظͨ͠εϐʔνΞχϝʔγϣϯ Λੜ੒ Իૉϥϕϧͷೖྗγʔέϯε͔Βޱͷಈ͖·Ͱͷ ೚ҙͷඇઢܗϚοϐϯάΛֶश͢ΔεϥΠσΟϯ ά΢Οϯυ΢༧ଌࢠΛ࢖༻ ৽ͨʹೖྗ͞ΕͨԻ੠ʹରԠ͢Δ͜ͱ͕Մೳ
    طଘͷख๏ͱͷޓ׵ੑ͕͋Δ ςετࡁΈͷεϐʔΧʔʹ͍ͭͯͷఆྔධՁͱશ ͘৽͍͠εϐʔΧʔʹର͢Δओ؍ධՁʢϢʔβʔ ελσΟʣ ࡉ΍͔ͳײ৘Λදݱ͢Δʹ͸͸Δ͔ʹେ͖ͳεέʔ ϧͷχϡʔϥϧωοτϫʔΫΛ܇࿅͠ͳ͚Ε͹ͳ Βͣඅ༻ରޮՌͷߴ͍แׅతͳσʔληοτඞཁ Siggraph’17 'UNB
    A Deep Learning Approach for Generalized Speech Animation
17. 
 ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ϦΞϧλΠϜإ໘ϞʔγϣϯΩϟϓνϟͱΞΠτ ϥοΩϯάΛ༻͍ͯ73ͰϏσΦձٞΛՄೳʹ͢ Δը૾ϕʔεͷख๏ ୯؟ը૾͔ΒϦΞϧλΠϜͰإͷΞχϝʔγϣϯ Λ࠶ߏ੒ Ξχϝ͕ϦΞϧ ࣮ࡍͷϏσΦͱ߹੒͞Εͨग़ྗͱͷؒͷ৭ࠩͷఆ ྔධՁʢإ໘Լ෦ʣͱɺઐ໳ՈਓͷϢʔβʔ ελσΟ Ξχϝʔγϣϯʹ͓͚Δ಄෦ͷಈ͖͕ݻఆ͞ΕΔ
    ͷͰɺ൅ͷໟΛؚΉΞΫλʔͷഎܠͷ࠶߹੒ͷݚ ڀ͕ඞཁ ࣍ͷεϥΠυͷ࿦จʢ͜ͷஶऀͷࠓ೥ͷ 4*((3"1) ͷ΍͕ͭಡΈ͔͚ͨͬͨͲΞϒετ ͔͠ݟ͔ͭΒͳ͔ͬͨɺಈը͸͋ͬͨ 'UNB
    Siggraph’17 FaceVR: Real-Time Gaze-Aware Facial Reenactment in Virtual Reality
18. 
 ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ಑ମɺ಄෦ɺද৘ɺࢹઢͷϦΞϧλΠϜ࠶ݱ͕Մ ೳͳ঻૾ըϏσΦͷੜ੒ ιʔεΞΫλʔͷ಄෦ͷಈ͖ͱ಑ମͷಈ͖Λϩό ετʹ௥੻ιʔεΞΫλʔͷಈ͖ΛλʔήοτΞ ΫλʔʹϑΥτϦΞεςΟοΫʹϚοϐϯά ϑϧϖʔύʔಡΉ
    ͑ͯ Siggraph’18 'UNB
    HeadOn: Real-time Reenactment of Human Portrait Videos
19. 
 ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ੩ࢭը͔ΒࣗಈతʹΞχϝʔγϣϯΛੜ੒͢Δٕ ज़ɹϏσΦͷإͷಈ͖Λ੩ࢭըʹରͯ͠ద༻͠ײ ৘දݱΛՄೳʹ͢Δ ૉࡐͱͳΔϏσΦͷಈ͖Λ໛฿͢Δ̎%ϫʔϓΛ ར༻ͯ͠঻૾ըΛΞχϝʔτՃ͑ͯ͜Ε͚ͩͰ ಘΒΕͳ͍γϫ΍ޱ಺ͳͲͷ৘ใ௥Ճ λʔήοτͷը૾͸Ұຕ͚ͩͰ͓L Πϯλʔωοτ্ͷ঻૾ըΛ࢖༻ͯ͠Ξχϝʔγϣ ϯΛ࡞੒
    ϢʔβʔελσΟ ಄෦ͷಈ͖͸੍ݶ͞ΕΔτϥοΧʔͷੑೳʹࠨ ӈ͞ΕΔ໨Λดͨ͡ঢ়ଶ͸࠶ݱ͖͠Εͳ͍ ·͋ ॠ͖͸ߴ଎ͳͷͰ໰୊ͳ͍ͱ΋ݴ͑Δ 'UNB
    Bringing Portraits to Life
20. 
 ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ैདྷͷإೝٕࣝज़๊͕͍͑ͯͨԣإ΁ͷऑ͞Λղ ফ͢ΔͨΊͷԣإτϨʔχϯάηοτ֦ுΞϓϩʔ ν ैདྷ͸ਖ਼໘إͱԣإͷ܇࿅ྔ͕ҧ͍͍͗ͯͨ͢ͷ Ͱɺಛ௃ۭؒʹ͓͍ͯ౳ՁͳϚοϐϯάΛ࣮ߦ͠ ͯζϨΛղফ ࣮૷͕؆୯͔ͭܰྔͳɺԣإ΁ͱରॲΞϓϩʔν Λͱ͍ͬͯΔ఺ ԣإ͔Βಛ௃ΛϚοϐϯάͯ͠ਖ਼໘إΛ࠶ߏ੒͢ Δ͜ͱʹ੒ޭͨ͠ Pose-Robust Face Recognition via Deep Residual Equivariant Mapping 'UNB
    
21. 
 Fast Deep Matting for Portrait Animation on Mobile Phone ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ϞόΠϧػثͷͨΊͷϦΞϧλΠϜࣗಈσΟʔϓ ը૾ϚοςΟΠϯάͱͦΕʹجͮ͘Ξχϝʔγϣ ϯੜ੒ %//͕ը૾ηάϝϯςʔγϣϯΛߦ͏ϒϩοΫ ͱ༧ଌ͞Εͨૈ͍όΠφϦϚεΫΛਫ਼៛ʹ௚͢ϑΣ βϦϯάϒϩοΫͰߏ੒ ߴ଎σΟʔϓը૾ϚοςΟϯάωοτϫʔΫ͕Ϟ όΠϧͰ࣮૷Մೳ $16͓Αͼ(16Ͱಈ࡞ͤ͞ॲཧ଎౓Λܭଌ ͜ͷख๏Ͱ͸ೖྗը૾Λμ΢ϯαϯϓϦϯάͯ͠ ͍ΔͨΊ൅ͷໟͳͲͷࡉ͔͍෦෼ʹؔͯ͠͸ແཧ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ˌ'5."̍̔
22. 
 ܳज़දݱͷςΫενϟΛอ࣋͠ͳ͕ΒݩͱͳΔϏ σΦͱҐஔ߹੒͠ϙʔτϨΠτΞχϝΛੜ੒͢Δ χϡʔϥϧωοτϫʔΫϕʔεͷελΠϧసૹ ϑΥτελΠϧͷϖΞ͕༩͑ΒΕΔͱࣗಈతʹς Ϋενϟ߹੒ͷϑϨʔϜϫʔΫ΁ͷೖྗͱͯ͠࢖ ༻Ͱ͖ΔΨΠσΟϯάνϟϯωϧΛࣗಈੜ੒ બ୒͞Εͨܳज़తϝσΟΞͷࡉ͔͍ςΫενϟΛ อશͰ͖Δ ઌߦݚڀͱͷൺֱΛߦ͍͜ͷख๏͕Ҏલͷख๏ͷ ܽ఺Λվળ͍ͨ͠Δ͜ͱΛূ໌͢Δ ֹͷγϫ΍ޱ඘ͳͲॏཁͳҙຯΛ࣋ͭઢܗ৘ใΛ ͏·͘෼཭ͯ͠ѻ͍͖͠Εͳ͍ Example-Based Synthesis of Stylized Facial Animations ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ 4*((3"1)
     ˌ'5."̍̔
23. 
 ඇ୯؟୯਺ө૾ʢΠϯλʔωοτө૾ͳͲʣΛ༻ ͍ͯإͷԋग़Λࣗಈతʹ࠾औ͠࠶ߏ੒͢Δγες Ϝ
    إΛ௚઀Ωϟϓνϟʔ͢Δͷ͸ࠔ೉ͳͷͰϏσΦ ϕʔεͰΩϟϓνϟʔ͠࠶ߏ੒
    إͷಛ௃ͱӨͷ৘ใͷ૒ํΛ࠶ߏ੒ʹར༻ إͷ࠶ߏ੒ͷਖ਼֬ੑ͕େ෯ʹ޲্ͨ͠ Πϯλʔωοτ্ͷಈըͰγεςϜΛςετͨ͠ ͱ͜Ζ੍ޚ͞Ε͍ͯͳ͍র໌ԼͰ΋ݸਓؒͷܗঢ় ࠩ͸ࠀ෰Ͱ͖ͨɹઌߦݚڀͱͷ݁Ռൺֱ ൅ͷໟɺϝΨωɺख΍ଞͷϑΝΫλʹΑ્ͬͯ· ΕΔͱ۠ผͰ͖ͳ͍ Automatic Acquisition of High-fidelity Facial Performances Using Monocular Videos ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ˌ'5."̍̔
24. 
 ਓؒͷಈ࡞ͷ̐%ʢ̏%QMVT࣌ؒʣεΩϟχϯά σʔληοτͷ࡞੒ɹ
    IUUQEGBVTUJTUVFNQHEF
    ͜͜Ͱ࢖͑ΔΑ
    ୹ڑ཭Ϩϯδͱ௕ڑ཭Ϩϯδͷ૊Έ߹Θͤ
    ςΫενϟ৘ใΛར༻ͯ͠δΦϝτϦϕʔεͷख ๏Λ֦ு γϯϓϧ͔ͭਖ਼͔֬ͭϙʔλϒϧ
    ࣮෺ͷεΩϟϯͱͦͷ಺෦ͷਅ࣮Λಉ࣌ʹఏڙ͢ Δσʔληοτ͸͜Ε͕ॳ ද໘͔ΒNNҎ্ͷزԿֶతͳΤϥʔ ٬؍తͳ ຊ෺ͱൺֱͨ࣌͠ͷΠϝʔδͷΤϥʔ ಈ͖ͷҰ கੑΛఆྔධՁͱͯ͠ϥϕϧ෇͚ Dynamic FAUST: Registering Human Bodies in Motion ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ˌ'5."̍̔ $713`
25. ֤ҥ෰Λࣗಈతʹ෼ׂ͠ɺ਎ମͷ࠷௿ݶͷϞσϧ Λੜ੒͠ɺ࣌ؒܦա͝ͱͷ෰ͷมܗΛ௥੻
    ಈ͘༸෰ͷߴղ૾౓̏%εΩϟϯ
    ෰ͷγϡϛϨʔγϣϯ͔Βҥ෰ͷऔΓࠐΈ΁ߟ͑ Λγϑτ̏%εΩϟϯʹద߹͢Δ৽نͷϚϧνϝο γϡදݱΛಋೖҥ෰ͷηάϝϯςʔγϣϯ ҥྨͷηάϝϯςʔγϣϯͱϞσϧͷମܕʹয఺ Λ౰ͯͨλʔήοτมߋˠԾ૷ࢼண͕Մೳ ଟ༷ͳҥ෰ͷ෼ੳɺλʔήοτมߋɺମܕมߋɺ Ωϟϓνϟͨ͠υϨεͷσʔληοτΛར༻ɺҰ ຕͷը૾͔Βҥ෰Λੜ੒ɹҎ্Λ࣮ߦͨ͠ Ϙλϯ΍ۗͳͲʹಛ௃͕͋Δෳࡶͳҥ෰ɺεΧʔ τ΍ωΫλΠͳͲମͷಈ͖ͱඞͣ͠΋Ұக͠ͳ͍ ΋ͷɺମͱ෦෼తʹ͔͠઀͠ͳ͍෰ͳͲ՝୊ ClothCap: Seamless 4D Clothing Capture and Retargeting ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ˌ'5."̍̔ 4*((3"1)
    
26. 
 The Noh mask effect: vertical viewpoint dependence of facial expression ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ೳͷ఻౷ʹख़ୡͨ͠ആ༏͕૷ண͢ΔϑϧϑΣΠε ϚεΫ͸಄ͷಈ͖͕มԽ͢Δͱ༷ʑͳදݱޮՌΛ ༠ൃ͢Δ͜ͱ͕ग़དྷΔ
    ೔ຊͱΠΪϦεͷࢹௌऀʹରͯ͠ϢʔβʔελσΟ Λߦ͍͜ͷޮՌΛݕূͨ͠ ϢʔβʔελσΟͷ݁Ռ͔Β͜ͷޮՌ͸จԽʹΑͬ ͯௐ੔͞Ε͍ͯΔ͜ͱ͕Θ͔ͬͨ
    ϨʔβʔͰͷղੳʹจԽతͳҙຯ͸ͳ͍ ҟͳΔจԽʹଐ͢Δूஂʹରͯ͠ௐࠪΛߦͬͨ
    ̏%ϨʔβʔεΩϟϯΛ༻͍ͯೳ໘ͷ಺෦ͷද৘ Λղੳ͠෼ੳʹ໾ཱͯΑ͏ͱͨ͠
    '5."
27. ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ छྨͷೳ໘Λ༻͍ͯӄӨ͕΋ͨΒ͢ද৘ೝ஌΁ ͷӨڹΛௐࠪ ૿ɼ
    தሡɼ
    ਂҪͷࡾछྨͷೳ໘Λਖ਼໘ɺԣɺ ্ɺԼͷ̐ํ޲͔ΒXిٿͰরࣹ͠ࡱӨɻେ ֶੜ໊̍̐̔ʹϢʔβʔελσΟ
    ೳ໘ͷද৘ೝ஌ʹ͓͚ΔӄӨͷޮՌ 'UNB
    
28. 
 ୯Ұͷ3(#ΧϝϥΛ༻͍ͯਓؒͷ׬શͳ̏%ࠎ֨ ϙʔζΛϦΞϧλΠϜʹΩϟϓνϟʔ͢Δ $//ʹجͮ̎͘%ͱ̏%ͷؔઅҐஔͷճؼͱӡಈ ֶతεέϧτϯϑΥονϯάΛ૊Έ߹ΘͤΔ ௿඼࣭ͳ3(#ΧϝϥͰ΋͓L
    Χϝϥͷੑೳͱܭࢉ࣌ؒͷ௕͞ͱ͍͏ೋͭͷ໰୊ ʹޮՌ ࠷ઌ୺ͷΦϑϥΠϯͷ3(#ΧϝϥΛ༻͍ͨख๏ ͱൺֱͨ͠ 73ͱ͔ίϯϐϡʔλʔήʔϜͷΠϯλϥΫγϣ ϯͱ͔ʹ࢖͑Δ
    ̎%δϣΠϯτ͸ζϨ͕ੜ͡Δ͜ͱ͋Γ໰୊
    VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5." 4*((3"1)`
29. 
 ୯؟3(#ΧϝϥͷΈͷγʔέϯεʹجͮ͘ϦΞ ϧλΠϜ̏%ϋϯυτϥοΩϯά DZDMF("/Λ༻͍ͯ߹੒ը૾Λม׵࣮ͯ͠ࡍͷख ը૾ʹԊ͏౷ܭ෼෍Λ༗͢Δʮ࣮ʯը૾Λੜ੒͠ SFHOFUͷ܇࿅ʹ࢖༻ ϋϯυτϥοΩϯάʹ͓͍ͯ3(#ͷख๏Ͱݱࡏ ͷ࠷ઌ୺ख๏Λ্ճΔύϑΥʔϚϯεΛൃش ݱࡏͷ࠷ઌ୺ख๏ͱͷग़ྗ݁Ռൺֱ 3FH/FU͕ྑ͍༧ଌΛಘΔͨΊʹۤ࿑͢Δͱτϥο Ωϯά͕ෆ҆ఆʹͳΔ͕ηάϝϯλಋೖͰରॲՄ
    ෳ਺ͷख͕઀͍ۙͯ͠ΔͱηάϝϯτෆՄ৴པ
    GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."
30. 
 ϚϧνΧϝϥΞϓϩʔνΛ༻͍ͯखͷؔઅͷΑ͏ ͳด࠹͠΍͍͢ΩʔϙΠϯτʹؔ͢Δݕग़ثΛ܇ ࿅͢Δ ϚϧνΧϝϥϏϡʔΛ༻͍ͯෳ਺ͷࢹ఺ͰϊΠζ ͷଟ͍ϥϕϧΛ੡࡞͠๛෋ͳσʔληοτΛߏங ࡏ໺ͷ3(#ը૾Λ༻͍ͯτϥοΩϯά͕Մೳ ਂ౓Λ༻͍ΔΞϓϩʔνͱޓ׵ੑͷ͋Δσʔληο τΛ༻੍͍ͯ౓Λൺֱ ख͕ӅΕ͍ͯΔͱݕग़ث͕Φʔόʔώʔτ Hand Keypoint Detection in Single Images using Multiview Bootstrapping ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."
31. '$//ʹը૾ϐΫηϧ͔ΒςϯϓϨʔτάϦου ΁ͷϚοϐϯάΛֶशͤ͞ճؼ໰୊ͱͯ͠ఆࣜԽ
    ࡏ໺ͷը૾ͷܗঢ়෼ੳɺมܗʹར༻ ܇࿅͞Εͨ$//Λ༻͍ͯϑΟʔυϑΥϫʔυܗ ࣜͰϚοϐϯά৘ใΛ࠶ߏ੒ ࣖͱ͔΋ਤΕΔ͠ମͷύʔπͷ෼ྨ΋Ͱ͖Δ 4UBUF
    PG
    UIF
    BSU
    ͷηάϝϯςʔγϣϯख๏ͱͷ ग़ྗ݁Ռൺֱ إҎ֎ͷਓؒͷ਎ମʹؔ࿈͢Δ࡞ۀʹରͯ͠΋؆ ୯ʹԠ༻Ͱ͖Δ
    ϖʔύʔͰ͸ΞϓϦ͕๛෋ͳإʹয఺ DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."
32. 
 ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ·ͱΊܥͷ࿦จ State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications
33. ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ػցֶशϕʔεͷιϑτηάϝϯςʔγϣϯʹΑͬ ͯݱࡏΞʔςΟετʹґଘ͍ͯ͠Δը૾ฤूʹ͓ ͚Δબ୒ͱ߹੒Λߦ͏ ͖ͪΜͱฤूʹ໾ཱͭΑ͏ͳҙຯͷ͋ΔڥքΛੜ ੒͢Δ͜ͱ͕Ͱ͖Δ σΟʔϓχϡʔϥϧωοτϫʔΫ͔Βͷߴ͍Ϩϕ ϧͷ৘ใΛೖྗը૾ͷϩʔΧϧͳςΫενϟʔ৘ ใͱ༥߹ͤ͞Δ ࣮ࡍʹݘͷը૾Λฤूͯ͠എܠΛม͑ͯΈͨ ਖ਼֬ͳιϑτηάϝϯςʔγϣϯΛߦ͏͜ͱ͕Ͱ ͖Δ͕଎౓͕஗͍ɹ Semantic Soft Segmentation Siggraph’18 'UNB
    
34. ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ χϡʔϥϧωοτΛ༻͍ͯը૾ͷަࠩ఺ͷಛ௃ྔ Λղੳͯ͠ը૾ؒͷରԠؔ܎Λݕग़Ԡ༻͢Δͱ ΩϚΠϥͷը૾ͱ͔࡞ΕΔ ͋Β͔͡Ί܇࿅͞Εͨ෼ྨωοτϫʔΫʹΑΔ σΟʔϓಛ௃Ϛοϓʹجͮ͘
    ֤ϨϕϧͰݕࡧྖҬΛߜΓࠐΈ
    ਓؒʹΑΔରԠؔ܎ݕग़ͱඇৗʹ͍ۙಈ࡞͕Ͱ͖ Δ ୅ସݚڀͱͷൺֱͱϢʔβʔελσΟ ෼ྨҎ֎ͷλεΫͷͨΊʹ܇࿅͞Εͨωοτϫʔ ΫͰͷ࣮૷΋ߦ͍͍ͨ Neural Best-Buddies: Sparse Cross- Domain Correspondence Siggraph’18 'UNB
    
35. ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ΦϑϥΠϯͷ೾ܗσʔλΛར༻ͨ͠ίϯϐϡʔλʔ ΞχϝʔγϣϯͷͨΊͷԻ੠߹੒ ΞίʔεςΟοΫͳԻڹάϦουΛ࢖༻
    େن໛ͳΠϯλʔϑΣʔεΛ࢖༻ ෳࡶͳݱ৅ʹରͯ͠ԻΛ߹੒Ͱ͖ਫͷԻͱ͔៉ྷ ʹදݱͰ͖Δ
    ΋ͷ͍͢͝୔ࢁΤϑΣΫτ͕͔͚ΒΕΔ ઌߦݚڀͱ͜ͷख๏Ͱಉ͡΋ͷΛग़ྗͯ݁͠ՌΛ ൺֱ ஗͍ͱ͍͏࠷େͷ໰୊͕͋Δ Toward Wave-based Sound Synthesis for Computer Animation
36. ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ༷ʑͳΩϟϥΫλʔʹॴ๬ͷಈ࡞ΛߦΘͤΔֶश ϕʔεͷΩϟϥΫλʔΞχϝʔγϣϯ
    ϞʔΩϟϓͰࡱͬͨεϐϯͱ͔ߴ౓ͳಈ͖ग़དྷΔ ΞʔςΟετ΍ϞʔγϣϯΩϟϓνϟͷആ༏͕ج ४ಈ࡞ͷσʔληοτΛఏڙɺ͔ͦ͜Βݱ࣮తͳ ಈ࡞Λੜ੒ ֶशϕʔεͰࣗવͳಈ͖Λ࣮ݱෳ਺ͷΫϦοϓ Λ౷߹ͯ͠ෳ਺ͷεΩϧΛ࣮ߦͰ͖Δ
    ͍ΖΜͳΩϟϥͰಈ͔ͤΔ ׬શͳख๏ͱҰ෦ͷίϯϙʔωϯτΛແޮʹ͢Δ ୅ସ͑ͷτϨʔχϯάεΩʔϜΛ༻͍ͯ݁ՌΛൺ ֱ Ґ૬ม਺Λج४ಈ࡞ͱಉظͤ͞Δඞཁ͕͋ΔͷͰ ಈ࡞ͷλΠϛϯάΛௐ੔͢Δೳྗ੍͕ݶ
    ֶशʹ͕͔͔࣌ؒΔ
    DeepMimic: Example-Guided Deep Reinforcement Learning of Physics- Based Character Skills Siggraph’18 'UNB
    
37. ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ωϟϓνϟʔͨ͠ಈ͖Λίϯτϩʔϧ͢ΔͨΊͷ χϡʔϥϧωοτϫʔΫ
    ಈ͖Λ༧ଌ͢ΔωοτϫʔΫͱήʔςΟϯάωο τϫʔΫΛ߹Θͤͨ΋ͷ
    ϞʔγϣϯΩϟϓνϟͷσʔλΛ༻͍ͨFOE
    UP
    FOEͷػցֶशಈ͖༧ଌͷωοτϫʔΫͱήʔ ςΟϯάωοτϫʔΫΛ߹Θͤͨ ಈ͖͕ਖ਼֬
    ಛʹޙΖ଍ͷಈ͖͕ࣗવ Ϟʔγϣϯ੍ޚͷػೳΛ࣋ͭଞͷϑϨʔϜϫʔΫ ͱग़ྗ݁ՌΛൺֱ
    ·ͨॏΈ෇͚ͷมԽΛه࿥ͨ͠ σʔληοτͷಛ௃্؆୯ͳಈ࡞Λ߹੒͢Δͷʹ ͸޲͍͍ͯΔ͕ശ͔Βඈͼ߱ΓΔͳͲͷμΠφϛο Ϋͳಈ࡞ʹ͸޲͔ͳ͍ Mode-Adaptive Neural Networks for Quadruped Motion Control
38. 
 GhostID:Enabling Non- PersistentUser Differentiation in Frequency2Division Capacitive ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ৴߸ΰʔετΛղੳͯ͠ಉ࣌ʹը໘ʹ৮Ε͍ͨΔ ࢦͷॴ༗ઌΛ۠ผͰ͖Δ प೾਺ଟॏ෼ׂʹΑͬͯ༰ྔΛଌఆ ֎෦ͷϋʔυ΢ΣΞʹґଘͤͣηϯαࣗମ͕λο νΛ۠ผͰ͖Δ ҰਓͷϢʔβʔೋຊͷࢦɺਓ̎ຊɺਓ̏ຊɺ ਓ̏ຊͰ࣮ݧͯ͠ߴ͍ਖ਼֬ੑΛಘͨ ֎෦ϋʔυ΢ΣΞʹґଘ͠ͳ͍ͱΰʔετ৘ใͩ ͚Ͱ͸ϢʔβʔΛ۠ผׂͯ͠Γग़͢ͱ͜Ζ·Ͱ͸ ແཧ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."̍̔ 6*45
    
39. 
 ༷ʑͳ෺ମ΁ͷλονೖྗΛՄೳʹ͢Δ௿ίετ ͳݕग़ٕज़ ҆Ձͳಋిੑࡐྉͱڠௐͯ͠ిքτϞάϥϑΟʔ Λ࢖༻ େ͖ͯ͘ෆنଇͳ΋ͷʹ΋ద༻Մೳ
    ༰қ͔ͭ҆Ձ ໊ͷࢀՃऀΛू͍ిۃ਺ɺࡐྉɺද໘ܗঢ়ͳ ͲͷཁૉΛมߋͯ͠ηϯγϯάٕज़ͱ௥੻ٕज़ͷ ςετΛߦͬͨ Electrick: Low-Cost Touch Sensing Using Electric Field Tomography ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ؀ڥཁҼʹΑΔి࣓ϊΠζ͕τϥοΩϯάͷੑೳ ʹӨڹ
    ͋·Γখ͍͞ͱిྲྀͷྔ͕খ͘͞ͳΓ࣭௿Լ
    '5."̍̔ 6*45
    
40. 
 ࢦʹऔΓ෇͚ͨ਒ಈηϯαͱεΫϦʔϯԠ౴Λݕ ग़͢ΔͨΊͷϑΥτμΠΦʔυΛ࢖ͬͯϨΠςϯ γΛଌఆٴͼఏࣔ ࢦͱը໘ʹ૷උ͢Δϋʔυ΢ΣΞΛ࢖༻
    ιϑτ΢ΣΞͱ૊Έ߹ΘͤΔ ܇࿅͕͍Βͳ͍ 8JOEPXT
    ͱɹ.BD
    PTY
    ͱɹVCVOUV
    ͷ༗໊ͳ ϗετίϯϐϡʔλ̏ͭͰಈ࡞ͤͨ͞
    ϔϧπ਺΍ݴޠͳͲ΋ҡ͍࣋ͯӨڹΛݕূͨ͠ ଞͷख๏ͱ૊Έ߹ΘͤΔ͜ͱͰγφδʔΛੜΉ Characterizing Latency
 in Touch and Button-Equipped Interactive Systems ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."̍̔ 6*45
    
41. 
 ݱࡏΞʔςΟετ͕ߦͳ͍ͬͯΔΞχϝʔγϣϯ ΁ͷೋ࣍తͳϞʔγϣϯ௥ՃΛΠϥετͷΩϟϥ Ϋλͷಈ͖Λ఻೻͢Δ͜ͱʹΑͬͯߦ͏ ಈ͔͢ͷ͕؆୯ ϢʔβʔελσΟΛߦ͍ັྗతͳೋ࣍తΞχϝʔ γϣϯΛ࡞੒͢Δͷʹ໾ཱͭ͜ͱΛࣔͨ͠ ҟͳΔϙʔζ·ͨ͸ϙδγϣϯʹ͋ΔΩϟϥΫλʔ Λೋ࣍ͳΞχϝʔγϣϯʹऔΓࠐΉʹ͸·ͩݚڀ ͕ඞཁ Secondary Motion for Performed 2D Animation ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ 6*45
     '5."̍̔ ΩϟϥΫλͷҟͳΔύʔπؒͰࣗಈతʹϞʔγϣ ϯΛ఻೻͢ΔϦάͷηοτΛఆٛ
42. ॳ৺ऀͱϓϩͷ૒ํ͕ૉૣ͘Ϛεεέʔϧͷ෺࣭ Λ߹੒Ͱ͖ΔֶशϕʔεͷγεςϜ ϦΞϧλΠϜͰߴ඼࣭ͷը૾༧ଌʹΑΓϨϯμϦ ϯάʹ͔Βඵ͔͔Δ໰୊ʹରॲࡾͭͷΞϧ ΰϦζϜΛ૊Έ߹ΘͤͯՄೳੑΛ֦ு ϨϯμϦϯά͕ૉૣ͍
    ௚ײతʹϚςϦΞϧΛબ୒Ͱ͖Δ ༧ଌͱ࣮ࡍͷϨϯμϦϯάͷࠩҟΛࢹ֮Խͯ͠ݕ ূ কདྷతʹ͸ֶशΞϧΰϦζϜͷ૊Έ߹Θͤ΍༰ྔ ͳͲͰਫ਼౓͕͞Βʹ্͕Δ͜ͱΛظ଴ Gaussian Material Synthesis ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."̍̔ 4*((3"1)
    
43. 
 Interactive Sound Rendering on Mobile Devices using Ray-Parameterized Reverberation Filters '5." ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ಈతͳγʔϯʹରͯ͠ଥ౰ͳα΢ϯυΛੜ੒͢Δ ͜ͱͷͰ͖Δ৽ͨͳα΢ϯυϨϯμϦϯάύΠϓ ϥΠϯ73ɺ"3ͷݱ࣮ײΛߴΊΔͷͱ͔ʹ࢖͑ Δ
    ৞ΈࠐΈϕʔεͰ͸ͳ͘ਓ޻తͳۭؒ࢒ڹΛϕʔ εʹ͍ͯ͠Δ εϚϗʹಈతͳԻ੠఻೻ޮՌΛ༩͑Δ͜ͱͷͰ͖ Δ࠷ॳͷΞϓϩʔν
    ৞ΈࠐΈϕʔεͷϨϯμϦϯάΑΓ΋ૣ͍ ఻౷తͳख๏ͱ͜ͷख๏ͷͦΕͧΕʹ͍ͭͯσε ΫτοϓͰͷಈ࡞Λൺֱͨ͠
    ͔͔Δ࣌ؒతίετ͕ѹ౗తʹগͳ͍
    ৞ΈࠐΈϕʔεͰ͸ͳ͘ਓ޻తͳ࢒ڹΛར༻ͯ͠ ͍ΔͨΊʹϔϧπ਺͕௿͍෦෼ʹରͯ͠ύϑΥʔ Ϛϯεͷࡉ΍͔͞ͱӶ͕͞଍Γͳ͍ 8BWFOFU΋͏গͪ͠ΌΜͱಡΈࠐΈ͍ͨͱࢥͬ ͨ CARL SCHISSLER, DINESH MANOCHA, University of North Carolina at Chapel Hill
44. 
 SweepCanvas:
 Sketch-based 3D Prototyping on an RGB-D Image ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ΤϯυϢʔβʔʹͱͬͯΠϯλϥΫςΟϒͳεέο νϕʔεͷ̏%ϞσϦϯάπʔϧ 3(#%৘ใΛ׆༻ͯ͠ฏ໘ղੳ৘ใ͔ΒϢʔβʔ ͷඳ͍ͨετϩʔΫΛ̏%ʹݻఆ.3'ϕʔεͷ ࠷దԽʹΑΓ̎ͭͷετϩʔΫ͔Βਝ଎ʹ̏%ϓ ϨʔϯΛੜ੒ ໘౗ͳઐ໳ٕज़Λඞཁͱͤͣ௚ײతͰΤϯυϢʔ βʔʹͱͬͯ࢖͍΍͍͢ ਓͷϢʔβʔʹଞͷ̐ͭͷख๏΋ؚΊͯϞσϦ ϯάΛߦͬͯ΋Β͍݁Ռͱ͔͔ͬͨ࣌ؒΛൺֱ ࠓޙ͸༷ʑͳϝσΟΞʹରͯ͠ࢼ͍ͨ͠ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ 6*45
     '5."̍̔
45. 
 ୯؟ΧϝϥΛ༻͍ͨඃࣸମʹϐϯτ͕߹͍ͬͯΔ Α͏ʹݟ͑Δൣғ͕޿͍ϦΞϧλΠϜτϥοΩϯ άٕज़
    ϚʔΧʔʹΑΔ௥੻͚ͩͰ͸ਫ਼౓͕௿͍ͷͰϑϨʔ ϜؒΞϥΠϝϯτͱߴີ౓ϙʔζϦϑΝΠϯϝϯ τΛ૊Έ߹ΘͤΔ ༰қʹೖखՄೳͰ૊Έཱͯ΍͍͢෦඼Λ༻͍͍ͯ Δ
    ෳ਺ͷΧϝϥΛ༻ҙ͠ͳ͓ͯ͘L %ͷ͓ֆ͔͖ʹ΋̏%ͷ͓ֆ͔͖ʹ΋Ԡ༻Ͱ͖ Δ DodecaPen: Accurate 6DoF Tracking of a Passive Stylus ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ 6*45
     '5."̍̔ ߹੒σʔληοτͱ࣮σʔληοτͷ྆ํΛ༻͍ ͍ͯτϥοΩϯάΛσεΫτοϓͰಈ࡞ͤ͞ PQUJUSBDLϞʔγϣϯΩϟϓνϟγεςϜͱൺֱ
46. 
 Ϣʔβʔ͕›ϏσΦͷதʹ͋Δॏཁͳίϯς ϯπશͯΛଊ͑ΒΕΔΑ͏ʹΧοτ͝ͱʹࢹքΛ ௐ੔͢Δ
    ϘλϯͰௐ੔Մ
    ϏσΦͷதʹ͋ΔॏཁͳίϯςϯπͷҐஔΛࣔ͢ ϥϕϧΛ࡞੒ ॏཁͳίϯςϯπ͸ෳ਺͋Δ͜ͱ͕ଟ͍͕ͦΕΛ શͯԣஅͰ͖ΔΞϓϩʔν ̐ͭͷ৽͍͠ϏσΦΛؚΉ̍̍ͷϏσΦʹ͍ͭͯɺ γϣοτௐ੔Λߦ͏ͨΊʹඞཁͳγϣοτڥքͷ ൑ผΛ֬ೝ ݱ࣌఺Ͱ͸γϣοτͷํ޲͔͠ߟྀ͍ͯ͠ͳ͍͕ ଞͷ͜ͱʹ͍ͭͯ΋΍Γ͍ͨ
    ޲͍ͯΔ͚࣌ͩίϯςϯπ͕࠶ੜ͞ΕͨΓͱ͔ Shot Orientation Controls for Interactive Cinematography with 360o Video ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."̍̔
47. 
 73Ͱߴղ૾౓ͳύϊϥϚө૾ΛζʔϜͯ͠୳ࡧ ͢Δํ๏ ࢹ໺ͷதԝʹը૾Λ֦େͨ͠ࢹ֮త'07Λ੍ݶ ͠ྖҬͷ֎ʹ֦େ͞Ε͍ͯͳ͍ഔମΛՃ͑Δ͜ͱ ͰγϡϛϨʔλͷΤϥʔΛ཈͑Δ ζʔϜͯ͠΋ಈ͖ͷ໰୊͕େ͖͘ൃੜ͠ͳ͍ Ϣʔβʔ͕ඵؒͷؒʹλʔήοτΛݟ͚ͭΒ ΕΔ͔ςετͯ͠Έͨ ߴղ૾౓ͩͱӅΕڻ͖ཁૉͱ͔ೖΕΒΕͯදݱͷ ෯͕޿͕Δ Panning and Zooming High-Resolution Panoramas in Virtual Reality Devices ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."̍̔
48. ›ը૾Ͱࢹքͷ֎ʹ͋Δؔ৺౓ͷߴ͍ίϯς ϯπΛQJDUVSF
    JO
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    Ͱදࣔ 1*1ͷҐஔٴͼ޲͖Λ30*ʢSFHJPO
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    ਂ౓ܭଌ 30*ΛGˇը໘͍දࣔͤ͞ΔͨΊͷ࠷খԽ͞Εͨ Ξϓϩʔν 1JDUVSF
    JO
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    ͷ͋Δͳ͠ͰϢʔβʔελσΟ ͠ɺϢʔβʔͷ৘ใड༰౓Λௐࠪ ϏσΦͷෳࡶੑ͕ͱͯ΋্͕Δͱ͋Μ·ΓޮՌͳ ͘ͳΔ Outside-In: Visualizing Out-of-Sight Regions-of-Interest in a 360 Video Using Spatial Picture-in-Picture Previews ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ '5."̍̔ 6*45
    
49. DeepHolo: Recognizing 3D Objects using a Binary-weighted Computer-Generated Hologram Naoya Muramatsu University of Tsukuba Pixie Dust Technologies, Inc. sh.mn.nat@gmail.com Chun Wei Ooi University of Tsukuba made3102@gmail.com Yoichi Ochiai University of Tsukuba Pixie Dust Technologies, Inc. wizard@slis.tsukuba.ac.jp Yuta Itoh University of Tsukuba s1711428@s.tsukuba.ac.jp ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ϙΠϯτΫϥ΢υϞσϧ͔Β࠶ߏ੒͞Εͨίϯ ϐϡʔλʔੜ੒ϗϩάϥϜΛѻ͏%//̏%఺܈ ϑΥʔϜΛ̎%σʔλͱͯ͠ॲཧͰ͖Δ %ϐΫηϧΫϥ΢υϞσϧΛ̎%ϗϩάϥϜσʔ λʹม׵͢Δ$()ϓϩάϥϜͷ࣮૷ ಛ௃Λऔಘ͢ΔύϥϝʔλʔΛऔΓ෷͏͜ͱ͕Ͱ ͖ΔͨΊؔ࿈ݚڀͷൺ΂ͯωοτϫʔΫ಺ʹ͓͍ ͯ͸Δ͔ʹεϖʔεޮ཰͕ྑ͍ ͍͔ͭ͘ͷ%//ͱೝࣝ݁ՌΛൺֱ
    ମੵଌఆɺଟࢹ఺ɺϐΫηϧΫϥ΢υϞσϧʹ͓ ͚Δ୅දతͳηοτͱൺֱ $()͕ϑϧ)%ͰͰ͔͍
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50. 
 Convolutional neural network-based regression for depth prediction in digital holography '5." ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ Tomoyoshi Shimobaba Graduate School of Engineering Chiba University1-33 Yayoi-cho, Inage-ku, Chiba, Japan shimobaba@faculty.chiba-u.jp Takashi Kakue Graduate School of Engineering Chiba University1-33 Yayoi-cho, Inage-ku, Chiba, Japan t-kakue@chiba-u.jp Tomoyoshi Ito Graduate School of Engineering Chiba University
 1-33 Yayoi-cho, Inage-ku, Chiba, Japan itot@faculty.chiba-u.jp ิ଍͞ΕͨϗϩάϥϜ͔Β̏࣍ݩۭؒ಺ͷ෺ମΛ ࠶ߏ੒͢ΔσδλϧϑΥτάϥϑΟʹඞཁͳਂ౓ Ґஔͷ༧ଌΛ$//ϕʔεͷճؼΛ༻͍ͯߦ͏ ϗϩάϥϜσʔληοτͰ͸ͳ͘εϖΫτϧσʔ ληοτΛ༻͍Δ ϛϦ୯ҐͰଌΕΔ
    ෼ྨ໰୊Ͱ͸ͳ͘ॏճؼ໰୊Λղ͘ͷͰ༧ଌਂ౓ ͕཭ࢄ஋Ͱ͸ͳ͘࿈ଓ஋ͱͳΔ ϗϩάϥϜσʔληοτΛ༻͍ͨͱ͜Ζ༧ଌਂ౓ ฏۉޡࠩ͸NN͕ͩͬͨεϖΫτϧσʔληο τΛ༻͍ΔͱNNͩͬͨ ͜Ε͔Β͸ҟͳΔҐஔʹ͋Δෳ਺ͷର৅෺ͷਂ౓ Λܭଌ͢Δ༧ఆ
51. A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print SYED SHAHRAM NAJAM*, AAMIR ZEB SHAIKH*, AND SHABBAR NAQVI** ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ৽ͨͳ౤ථγεςϜͷ࣮૷ͷͨΊͷ̎ͭͷ౤ථऀ ݕग़ٕज़ɹநग़ͨ͠ಛ௃ϕΫτϧΛσʔλϕʔε ʹొ࿥ͯ͋͠ΔόΠΦσʔλͱൺֱ ࢦ໲ೝূͱإೝূͷೋͭ૊Έ߹ΘͤΔ͜ͱͰਖ਼ղ ཰ΛΧόʔ ࣮ࡍʹൃల్্ࠃͷ౤ථγεςϜʹԠ༻Ͱ͖ͦ͏ ͦΕͧΕʹ͍ͭͯݕূ࣮ݧͨ͠ͱ͜Ζਖ਼ղ཰͕Ͳ ͪΒ΋Λ௒͑ͨ ҉߸ԽΞϧΰϦζϜΛಋೖͯ͠ηΩϡϦςΟΛ૊ Έࠐ·ͳ͍ͱ͍͚ͳ͍ '5."
52. 
 Joint Material and Illumination Estimation from Photo Sets in the Wild Tuanfeng Y. Wang Tobias Ritschel Niloy J. Mitra University College London, UK '5." ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ωοτ্ͷը૾Λར༻ͨ̎͠%Πϝʔδը૾ͷর ໌ɺܗঢ়ɺࡐྉͷૢ࡞ɹ൓ࣹͷ࠷దԽ
    Ԡ༻ͯ͠ը໘ʹ৽͘͠ҜࢠΛग़ͤͨΓ͢Δ
    ಉ͡র໌ͷԼʹ͋Δ༷ʑͳૉࡐͷը૾ͱҧ͏র໌ ͷԼʹ͋Δಉ͡ૉࡐͷը૾Λ࢖༻߹੒ը૾Ͱ܇ ࿅͞ΕͨχϡʔϥϧωοτϫʔΫͷ࠷దԽ ωοτͷը૾Λ࢖༻͍ͯ͠Δ ಉ༷ͷख๏ͱϢʔβʔελσΟͰຬ଍౓Λൺֱ͠ ͨ ޿͍෦԰ͷը૾࢖͏ͱޡ͕ࠩग़΍͍͢
    ೖྗଌఆ஋ͷαϯϓϧ਺͕ݶΒΕ͍ͯΔͱর໌ͷ ݟੵ΋Γࣗମ͕ෆࣗવʹݟ͑Δ͜ͱ͕͋Δɹɹɹɹɹ ΦϒδΣΫτ͕ۙ͗͢ΔͱมͳӨ͕ग़Δ
    
53. Leaked Light Field from Everyday Material: Designing Material Property Remained Light-field Display Kazuki Takazawa1∗ Kenta Suzuki1 Shinji Sakamoto1 Ryuichiro Sasaki 2 Yoshikuni Hashimoto2 Yoichi Ochiai1 ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ੴɺ໦ࡐɺڸͳͲͷෆಁ໌ࡐྉΛදࣔ໘ͱͯ͠࢖ ༻Ͱ͖ΔΑ͏ʹ͢Δ ςΩετΛೖྗ͍ͯͩ͘͠͞ ςΩετΛೖྗ͍ͯͩ͘͠͞ ςΩετΛೖྗ͍ͯͩ͘͠͞ ςΩετΛೖྗ͍ͯͩ͘͠͞ ਓؒͷ໨ʹ͸ݟ͑ͳ͍ЖNͷϐϯϗʔϧΛ࢖ ༻͠ϐϯϗʔϧσΟεϓϨΠΛ࣮૷ '5."
54. Materialization of Motions: Tangible Representation of Dance Movements for Learning and Archiving Mose Sakashita University of Tsukuba mose.sakashita@gmail.com Kenta Suzuki University of Tsukuba Ikuzus.atnek.0626@gmail.com Keisuke Kawahara University of Tsukuba kawahara@ai.iit.tsukuba.ac.jp Kazuki Takazawa University of Tsukuba 1220kazu1412@gmail.com Yoichi Ochiai University of Tsukuba wizard@slis.tsukuba.ac.jp ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ μϯεͷಈ͖ΛΧϝϥΛ༻͍ͯ̏%తʹΩϟϓνϟ ͠Ұఆͷςϯϙ͝ͱʹਓؒͷಈ͖Λ༗ܗͷਓܗΛ ༻͍ͯදݱɹμϯεͷֶशʹ໾ཱͯΔ ΩωΫτΧϝϥΛ࢖֤ͬͯؔઅͷYɺZɺ[ࡾ࣍ݩ ۭؒతҐஔΛऔಘ ̏%ϓϦϯλʔͷग़ྗΛਓؒͷಈ͖Λ٬؍తʹଊ ͑Δ͜ͱʹར༻ֶ͠शʹ໾ཱͯͨ
    ಈ͖ͱςϯϙͷؔ܎ੑΛར༻
    ࣮ࡍʹग़ྗͯ͠ΈͨΒͰ͖ͨ
    ʢϙελʔ͔͠ݟ͔ͭΒͳ͔͔ͬͨΒ͜͜Α͘Θ ͔Β͵ɺɺʣ '5." 4*((3"1)
     μϯεͷಈ͖͸ෳࡶ͔ͩΒԻָͷςϯϙͱಈ͖ͷ ৼΓ෇͚ͷؔ܎ੑΛར༻ͨ͠ 3D skeleton-based human action classification: A survey ͳΜ͚ͩͲ༗ྉͰಡΊͳ͍ɺͭΒ͍
55. 
 Dual Deep Network for Visual Tracking Zhizhen Chi, Hongyang Li, Student Member, IEEE, Huchuan Lu, Senior Member, IEEE, and Ming-Hsuan Yang, Senior Member, IEEE '5." ͲΜͳ΋ͷʁ ಉ͡ߏ଄Λ࣋ͪผͷॏΈ෇͚Ͱ܇࿅͞Εͨೋͭͷ ωοτϫʔΫΛ༻͍ͯϰΟδϡΞϧτϥοΩϯά ʹ͓͍ͯλʔήοτͱͳΔΦϒδΣΫτΛΑΓ໌ ֬ʹ൑ผ͢Δ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ύονΛ࢖ͬͯτϨʔχϯάσʔλΛ૿ڧֶͯ͠ शΛޮ཰Խɹ
    ϩʔɾϋΠΛ୲͏̎ͭͷωοτϫʔΫʹΑΔηϧ ϑڭࢣ෇ֶ͖श ᐆດͳڥք΍എܠ͔ΒλʔήοτͱͳΔΦϒδΣ ΫτΛΑΓ໌֬ʹ۠ผͰ͖ΔΑ͏ʹͳͬͨ ࣮ࡍʹಈ࡞ͤ͞ఆྔධՁΛେྔʹߦͬͨ $//͸ࣄલͷ܇࿅ʹґଘ͍ͯͨ͠Γɺλʔήο τͷςΫενϟΛͪΌΜͱೝ͍ࣝͯ͠ͳ͔ͬͨΓɺ पΓͷࣅ͍ͯΔ΋ͷͱ۠ผͰ͖ͳ͔ͬͨΓ͢Δɻ ͜ΕΛղܾ͔ͨͬͨ͠
    ͏ʔΜ
56. 
 Deep Learning for Action Recognition in Augmented Reality Assistance Systems Matthias Schro ̈der Neuroinformatics Group, Bielefeld University maschroe@techfak.uni-bielefeld.de Helge Ritter Neuroinformatics Group, Bielefeld University helge@techfak.uni-bielefeld.de ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ "3ϔουϚ΢ϯτσΟεϓϨΠͷ։ൃͷͨΊʹ Ϣʔβʔͷߦಈ͔Βநग़ͨ͠σʔληοτΛσΟʔ ϓʹಥͬࠐΈϑΟʔυόοΫ͢Δ େن໛ͳσʔλϕʔεͷߏஙʹՃ͑ͯϞόΠϧԽ ͍ͨ͠ͷͰ$//ΛαΠζͱޮ཰ʹ͓͍ͯ࠷దԽ ͍ͨ͠ ࣮ࡍʹಈ࡞ͤͨ͞Βจ຺ґଘͷϑΟʔυόοΫ͕ ༗ޮͰ͋Δ͜ͱΛଟ͘ͷݚڀऀ͕ೝΊͨ ϩʔΧϧͳର৅ೝ͔ࣝΒͷτϥοΩϯάͱάϩʔ όϧͳจ຺ґଘͰͲ͏΍ͬͨΒ͏·͘ಈ࡞͢Δ͔ ͷϑΟʔυόοΫΛ૊Έ߹ΘͤΔ ϢʔβʔͷΞΫγϣϯ͔ΒϑΟʔυόοΫ͢Δͱ ͍͏Ξϓϩʔνํ๏ Deep Appearance Maps ʢ2018ʣͱ͔ '5."
57. 
 ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Deep Appearance Maps Maxim Maximov 1 , Tobias Ritschel 2 , Mario Fritz 11 Max Planck Institute for Informatics, Saarland Informatics Campus 2 University College London ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ '5." ൓ࣹ཰΍র໌ύϥϝʔλΛநग़͢ΔͷͰ͸ͳ͘֎ ؍Λ௚઀දݱ͢ΔσΟʔϓχϡʔϥϧωοτϫʔ ΫͰॊೈʹ֎؍Λදݱ͍ͨ͠ ϐΫηϧϕʔεͷϨϯμϦϯάɺ࠶ߏ੒Λ࢖༻͠ ͳ͍͜ͱʹΑͬͯૉࡐΛॊೈʹදݱ͢Δ͜ͱ͕Ͱ ͖Δ ֎؍ͦͷ΋ͷΛදݱ͍ͯ͠ΔͷͰૉࡐʹؔͯ͠׮ ༰
    ϚςϦΞϧΛॏࢹ ؔ࿈ݚڀͰ࢖༻ͨ͠ωοτϫʔΫͱग़ྗ݁ՌΛൺ ֱ
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 that learn and think like people. Behavioral and Brain Sci. 40 (2017) 

58. WAVENET: A GENERATIVE MODEL FOR RAW AUDIO Aa ̈ron van den Oord Karen Simonyan Nal Kalchbrenner Sander Dieleman Oriol Vinyals Andrew Senior Heiga Zen† Alex Graves Koray Kavukcuoglu '5." ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ੜͷ೾ܗΛੜ੒͢ΔͨΊͷσΟʔϓχϡʔϥϧωο τϫʔΫɹҟͳΔ੠ͷಛ௃ྔΛಉ౳ͷ஧࣮౓Ͱิ ଍ɺ੾Γସ͑Δ͜ͱ͕Ͱ͖ΔɹࣝผϞσϧʹ΋࢖ ͑Δ
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62. Towards the Automatic Anime Characters Creation with Generative Adversarial Networks Yanghua Jin School of Computer Science Fudan University jinyh13@fudan.edu.cn Jiakai Zhang School of Computer Science Carnegie Mellon University jiakaiz1@andrew.cmu.edu Minjun Li School of Computer Science Fudan Univerisity Yingtao Tian Department of Computer Science Stony Brook University Huachun Zhu School of Mathematics Fudan Univerisity minjunli13@fudan.edu.cn yittian@cs.stonybrook.edu zhuhc14@fudan.edu.cn Zhihao Fang Department of Architecture Tongji Univerisity fangzhihao126@gmail.com ˌ'5." ͲΜͳ΋ͷʁ ٕज़΍ख๏ͷΩϞ ઌߦݚڀͱൺ΂ͯԿ͕͍͢͝ʁ ٞ࿦͸͋Δʁ Ͳ͏΍ͬͯ༗ޮͩͱূ໌ͨ͠ʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ΞχϝΩϟϥͷإը૾σʔληοτʹಛԽͨ͠ ("/ͷ܇࿅ํ๏ͷఏএ
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65. Full 3D Reconstruction of Transparent Objects Bojian Wu, Yang Zhou, Yiming Qian, Minglun Gong, Hui Huang ʹΞ͵΍͹ʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ٗढ़Ώघ๑͹ΫϠͺʹ͞ʃ ٠࿨ͺ͍Ζʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ಃ໎͵ΨϔζΥέφ͹3࣏ݫ͹ܙয়ΝࣙಊదͶϠυϩԿͤΖघ๑ɽ 201813559 ஓീ ֋౉ #2 (෻αʖη) ࠹ॵͶΨϔζΥέφ͹୉Ή͖͵ܙয়ΝϠυϩԿͪ͢ϧϓϠυϩΝ ࡠ੔͢ɼΨϔζΥέφͳިࠫͤΖޭત͹ଲԢؖܐΝ༽͏ͱɼϧϓ ϠυϩΝ࠹నԿ͢ͱ͏͚ɽ ϠυϩԿͪ͢΍͹ͳɼࣰࡏ͹ΨϔζΥέφͳ͹න໚͹ړ཯͹ޣࠫ Νܯ଎ͪ͢ɽ SIGGRAPH 2018 ॊཔݜڂͲͺಃ໎͵ΨϔζΥέφ͹2ͯ͹໚͹఼܊Νਫ਼੔ͤΖ͞ͳ ͺड़པͪ͗ɼͨ͹ܙয়ͺ෈׮સͫͮͪɽ͞͹घ๑Ͳͺɼ׮સ͵̑D ϠυϩΝ෰ݫͤΖ͞ͳ͗Ͳ͘Ζɽ ஦ۯ͹ಃ໎͵ΨϔζΥέφͺॴཀྵͲ͘͵͏ɽ ΨϔζΥέφͶଲ͢ͱɼޭત͹۸઄͹յ਼͗ଁ͓Ζͳޣࠫ͗୉͘ ͚͵Ζɽ Qian et al. 3D Reconstruction of Transparent Objects with Position-Normal Consistency. [2016]
66. MonoPerfCap: Human Performance Capture from Monocular Video WEIPENG XU, AVISHEK CHATTERJEE, and MICHAEL ZOLLHÖFER, Max Planck Institute for Informatics HELGE RHODIN, EPFL DUSHYANT MEHTA, HANS-PETER SEIDEL, and CHRISTIAN THEOBALT, Max Planck Institute for Informatics ʹΞ͵΍͹ʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ٗढ़Ώघ๑͹ΫϠͺʹ͞ʃ ٠࿨ͺ͍Ζʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ 201813564 ౖԲ༑ഇ #2 (෻αʖη) SIGGRAPH 2018 ୱءΩϟϧͲࡳӪ͠Ηͪӫ଀ΝخͶɾਕؔ͹ϛʖθ ͖Δ஥༽ͤΖҧෲΉͲΝ3D࠸ߑ஛ͤΖϜʖΩϪηϏ ϓΧʖϜϱηΫϡϕοϡٗढ़͹఑Ҍ ୱءΩϟϧͶΓͮͱࡳӪ͠Ηͪӫ଀͹ΊΝ೘ྙͳ͢ ͪॵΌͱ͹3DϏϓΧʖϜϱηΫϡϕοϡٗढ़Ͳ͍Ζɿ Sparse 2Dͳbatchϗʖη͹ࢡ੐ਬఈघ๑Ν༽͏ͪCNN ͶΓΖਕؔ͹3Dࢡ੐ݗड़Νࡀ༽͢ͱ͏Ζɿ ߻ܯ120ຌ͹ϑυΨέϨρϕΝ༽͏ͱϗϱοϜʖέ Νߨͮͪɿ࣯ద͵ݗৄͲͺɾસͱ͹ϗϱοϜʖέ υʖνιρφͶଲ͢ɾ׮સ͵݃ՎΝಚͪɿ ࠹నԿ͠Ηͱ͏͵͏CPUαʖχͲͺҲͯ͹೘ྙϓ ϪʖϞΝॴཀྵͤΖ͹Ͷ༁1.2෾Νགྷͪ͗͢ɾdata parallel optimizationΝ༽͏Ζ͞ͳͲ୉෱ͶրྒྷͲ͘ ΖɿগཔదͶͺϨΠϩν΢ϞॴཀྵΝ໪ࢨͤɿ Robertini et al. 2016
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79. ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ͲΜͳ΋ͷʁ Yi-Chen Chen1, Vishal M. Patel1, P. Jonathon Phillips2, and Rama Chellappa1 Dictionary-based Face Recognition from Video &$$7
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80. ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ͲΜͳ΋ͷʁ Dictionary-based Face and Person Recognition from Unconstrained Video Yi-Chen Chen, Student Member, IEEE, Vishal M. Patel, Member, IEEE, P. Jonathon Phillips, Fellow, IEEE, and Rama Chellappa, Fellow, IEEE *&&&
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     D ܗঢ়มԽΛ ൐͏ϢʔβʔମݧΛݚڀ͢Δཧ༝Λٞ࿦͢Δ ܗঢ়มԽΠϯλʔϑΣʔεͷಛੑͱɼσδλϧ৘ใͱͷΠϯλϥΫγϣϯΛڧԽ͢Δ͜ͱΛ໨ࢦ͍ͯ͠Δɻطଘͷ࡞ۀΛࢹ఺ʹೖΕɼաখධՁ͞Ε ͨํ޲Λಛఆ͢ΔͨΊʹɼܗঢ়มԽΠϯλʔϑΣʔεͷઃܭۭؒΛௐ͍ࠪͯ͠Δ࿦จ͸΄ͱΜͲͳ͍ɻܗঢ়มԽͷ৺ཧతɼܳज़తද໘ʹ͍ͭͯ͸΄ ͱΜͲٞ࿦͞Ε͍ͯͳ͍ɻ૬ޓ࡞༻ʹয఺Λ͋ͯΔ͜ͱ͸΄ͱΜͲͳ͘ɼ૬ޓ࡞༻ͷϞσϧʹܗঢ়มԽΛؔ࿈෇͚Δ͜ͱ͸΄ͱΜͲͳ͍ ܗঢ়มԽΠϯλʔϑΣʔεʹؔ͢Δͷ࿦จΛݕ౼ͨ͠ɻ࿦จ͸ɼૢ࡞͞ΕΔܗঢ়ͷͲͷଆ໘ ܗঢ়ɼମੵͳͲ ͓Αͼܗঢ়ؒͷม׵͕ͲͷΑ͏ʹୡ੒͞Ε ͔ͨʹؔͯ͠෼ੳ͞Εͨɻܗঢ়มԽͷ໨తɿଟ͘ͷΠϯλʔϑΣʔε͸৘ใ఻ୡΛ໨తͱ͍ͯ͠Δɻܗঢ়มԽ͕ਖ਼֬ͳίϛϡχέʔγϣϯͷͨΊͷྑ͍Ϟμ ϦςΟ͔Ͳ͏͔ͱ͍͏ٙ໰ʹ౴͍͑ͯΔ࿦จ͸كɻ͞·͟·ͳλΠϓͷม׵͕ͲͷΑ͏ʹ࢖༻͞ΕɼͲͷΑ͏ͳޮՌ͕΋ͨΒ͞ΕΔ͔Λମܥతʹௐ΂Δ͜ ͱ͕Ͱ͖ͳ͔ͬͨɻͷ࿦จ͸Ϣʔβʔମݧʹ͍ͭͯධՁ͍ͯ͠ΔɻϢʔβʔମݧͷ֓೦Խͱݚڀʹ͓͚Δ࠷৽ͷਐาΛ౿·͑ͯ࡞੒͞Εͨ΋ͷ͸΄ͱΜ Ͳͳ͍ɻ 
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105. “I don’t want to wear a screen”: Probing Perceptions of and Possibilities for Dynamic Displays on Clothing - CHI2016 Laura Devendorf, Joanne Lo, Noura Howell, Jung Lin Lee, Nan-Wei Gong, M. Emre Karagozler, Shiho Fukuhara, Ivan Poupyrev, Eric Paulos, Kimiko Ryokai (UC Barkeley, Google ATAP) "CTU *OUSPEVDUJPO $PODMVTJPO μΠφϛοΫςΩελΠϧσΟεϓϨΠ͕Ռͨ͢໾ׂΛ୳͍ͬͯΔɻ&CCͱݺ͹ΕΔ৽͍͠ςΩελΠϧσΟεϓ ϨΠٕज़Λ։ൃ͠ɼҥྨݻ༗ͷσβΠϯͷՄೳੑΛ୳ٻ͍͔ͨͭ͘͠ͷ৫Γͱ͔͗ฤΈͷ෍ݟຊΛ࡞੒ͨ͠ɻैདྷ ͷεΫϦʔϯϕʔεͷσΟεϓϨΠͱ͸ඇৗʹҟͳΔج४ʹैͬͯɼҥྨϕʔεͷσΟεϓϨΠͷັྗͱ༗༻ੑΛ ධՁͨ͠ɻ ٕज़͕࣍ୈʹ਎ʹ͚ͭΒΕΔΑ͏ʹͳΓɼεϚʔτͳΞΫηαϦͷྖҬΛ཭Εɼ෰ͷ෍஍ʹೖΓࠐΉʹͭΕɼ)$*Ͱ͸ٕज़త ͳσΟεϓϨΠ͕ݸਓతͳελΠϧͷதͰҭΉݸਓతͳҙຯͱࣾձతػೳΛߟྀ͢Δ͜ͱ͕ॏཁɻ͜ΕΒͷҙຯΛௐ΂ΔͨΊ ʹ&CCΛ։ൃ͠ɼσβΠφʔʹ࿅श΍ݸਓతελΠϧ΁ͷ૊ΈࠐΈํ๏Λઆ໌͢ΔΑ͏ґཔͨ͠ɻ ҥ෰ϕʔεͷσΟεϓϨΠ͕ɼෳࡶͰҙຯͷ͋ΔݸਓతͳελΠϧͷγʔϯͷதͰػೳ͢Δ͜ͱ͕Ͱ͖Δํ๏Λ໌Β͔ʹͨ͠ɻҙຯͷ͋Δίϛϡ χέʔλͱͯ͠ͷࡐྉΛऔΓೖΕͨϚςϦΞϧத৺ͷࢹ఺Λ࠾༻͢Δ͜ͱͰɼҥ෰ʹςΫϊϩδʔͷೝࣝΛܗ࡞ͬͨ࿈૝ΛҾ͖ग़͢͜ͱ͕Ͱ͖ ͨɻΩϟϯόεͷӅᄻ͕೔ৗ࢖༻ͷͨΊͷҥྨϕʔεͷσΟεϓϨΠͷઃܭεϖʔεΛ֦େ͠͏Δɻ&CCͷ஗͞ͱ௿ղ૾౓͕ࢥ͍͕͚ͳ͍ࣾձ తૺ۰ɼൽ೑ͳղઆɼݸਓσʔλͷᛉ૝ܦݧΛଅਐ͢ΔͨΊʹैࣄͰ͖ΔγφϦΦΛ૝ఆͨ͠ɻ 
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106. clayodor: Retrieving Scents through the Manipulation of Malleable Material - TEI2015 Cindy Hsin-Liu Kao, Sang-won Leigh, Ken Perlin, Ermal Dreshaj, Xavier Benavides, Hiroshi Ishii, Juditj Amores, Pattie Maes (MIT Media Lab, NYU Media Research Lab) "CTU *OUSPEVDUJPO $PODMVTJPO ΫϨΦυʔϧ͸೪౔ͷΑ͏ͳՄ஁ੑͷ͋Δ෺࣭ͰɼϢʔβʔ͕ͦͷܗঢ়Λૢ࡞ͨ͜͠ͱʹج͍ͮͯ೏͍͕มԽ͢Δɻ ܗঢ়มԽૉࡐͷ༗ܗੑΛ୳ٻ͠ɼҰ࣌త͔ͭແܗͷײ֮ೖྗͰ͋Δʹ͓͍Λิ଍͢Δɻ֓೦࣮ূϓϩτλΠϓͷ ઃܭΛఏࣔ͠ɼϑΥʔϜΛհͯ͠ʹ͓͍ΛφϏήʔτ͢Δ՝୊ʹ͍ͭͯٞ࿦͢Δɻ ࠷ۙͷ)$*ͷݚڀ͸ɼ੩తͰ͔͍ͨ෺ཧతΠϯλʔϑΣʔεΛӽ͑ͯɼಈతʹ੍ޚ͞ΕΔϚςϦΞϧʹҠߦͨ͠ɻలੑͷ͋Δແܗͷ ηϯαʔೖྗͰ͋Δ೏͍ΛัΒ͑ΔͨΊʹɼలੑͷ͋ΔࡐྉΛܗ࡞Δ͜ͱͷ༗๬ੑΛ୳ΔɻϢʔβʔ͕ࡐྉΛखʹ࣋ͬͯɼ༷ʑͳ ҙຯͷ͋Δܗʹ෺ཧతʹܗ࡞Δ͜ͱΛՄೳʹ͢Δ͜ͱͰɼܗΛʹ͓͍Λ݁ͼ͚ͭΔજࡏతͳਫ਼ਆϞσϧΛ୳ٻ͢Δ͜ͱΛ໨ࢦ͢ɻ ॊೈͳ৮֮ΠϯλʔϑΣʔεΛૢ࡞ͯ߳͠ΓΛݕࡧ͢ΔϓϩτλΠϓγεςϜΛ঺հͨ͠ɻѹྗ׬࣏ٕज़ͱ߳Γ߹ ੒ٕज़Λ༥߹ͤ͞Δ͜ͱͰɼզʑ͸༗ܗͷ૬ޓ࡞༻Λհͯ͠ҟͳΔ߳ΓΛհ͖ͨ͠Ίࡉ͔ͳφϏήʔγϣϯͷՄೳ ੑΛ໛ࡧͨ͠ɻ֤ϋʔυ΢ΣΞϞδϡʔϧͰߏ੒͞ΕΔϞδϡϥʔϒϩοΫΛ࡞੒͢Δ͜ͱ͕ϑϡʔνϟʔϫʔΫɻ 
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107. Wearability Factors for Skin Interfaces - AH2016 Xin Liu, Katia Vega, Pattie Maes, Joe A. Paradiso (MIT Media Lab) "CTU *OUSPEVDUJPO $PODMVTJPO ΢ΣΞϥϒϧΠϯλʔϑΣʔε͸ண༻ೳྗ͚ͩͰͳ͘ɼσόΠε͕զʑͱͲͷΑ͏ʹަྲྀ͢Δ͔ͱ͍͏఺ͰɼςΫϊϩδʔͱϑΝογϣϯͷ༥߹Λ࠶ߟ͢Δ ͜ͱʹͭͳ͕Δɻηϯα΍ଞͷίϯϐϡʔςΟϯάσόΠεΛ਎ମද໘ʹ௚઀औΓ෇͚Δ͜ͱͰɼ΢ΣΞϥϒϧΛεΩϯΠϯλʔϑΣʔεͱͯ͠ઃܭ͢Δ͜ ͱ΋Ͱ͖Δɻ΢ΣΞϥϒϧʹӨڹΛٴ΅͢΢ΣΞϥϏϦςΟͷཁҼΛεΩϯΠϯλʔϑΣʔεͷ؍఺͔Βݕ౼͢Δɻ΢ΣΞϥϏϦςΟཁҼ͸਎ମతଆ໘ͱ σόΠεͷଆ໘Ͱ෼ྨ͞ΕΔɻಛघޮՌϝΠΫʹຒΊࠐ·ΕͨϦδουͳϘʔυͱɼσόΠεʹ઀ଓ͞ΕͨεΩϯϚ΢ϯτ͞ΕͨॊΒ͔͍ϚςϦΞϧͱ͍ ͏ͭͷΠϯλʔϑΣʔεͷྫͰઆ໌͢Δɻ ண༻Մೳͳٕज़͸ɼҥ෰΍ΞΫηαϦʔʹݶఆ͞ΕΔ΂͖Ͱ͸ͳ͍ɻൽෘͱ൅΍௺ʹٕज़Λஔ͘͜ͱ͸ɼ਎ମ಺૷ஔͷ৽͍͠ՄೳੑΛ։͘ɼൽෘ΁ ͷ௚઀઀৮͸ɼੜ෺తηϯγϯάσʔλΛ༗ҙʹվળ͢Δ͜ͱ͕Ͱ͖Δɻ֎తγεςϜΛઈ͑ͣվળ͢Δ΢ΣΞϥϒϧͱ͸ҟͳΓɼൽෘΠϯλʔ ϑΣʔε͸ɼண༻ࣗ਎ͷ਎ମΛΠϯςϦδΣϯτ͔ͭ૿େͤ͞Δɻ΢ΣΞϥϒϧ΢ΣΞͷ΢ΣΞϥϏϦςΟϑΝΫλΛεΩϯΠϯλʔϑΣʔεͷͦ ΕʹϚοϐϯά͢Δɻ ΢ΣΞϥϒϧ΢ΣΞͷ΢ΣΞϥϏϦςΟʹ͍ͭͯใࠂ͞Ε͍ͯΔ࠷΋ҰൠతͳཁҼΛಛఆ͠ɼͦΕΒΛεΩϯΠϯλʔϑΣʔεͷ؍఺͔ Β෼ੳͨ͠ɻ਎ମతଆ໘ͱσόΠεతଆ໘ʹ͓͚Δண༻ՄೳੑͷཁҼΛ෼ྨͨ͠ɻεΩϯΠϯλʔϑΣʔεઃܭऀͷͨΊͷҰ࿈ͷ෯޿͍ ΨΠυϥΠϯΛఏڙ͢Δ͜ͱΛ໨తͱ͍ͯ͠Δɻɹ૷ஔ͕਎ମʹ઀৮͢Δ͜ͱʹΑΓɼΑΓਖ਼֬ͳ৽͍͠σʔλ͕ݕग़͞ΕΔՄೳੑ͕͋ Δɻ 
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108. iSkin: Flexible, Stretchable and Visually Customizable On-Body Touch Sensors for Mobile Computing - CHI2015 Martin Weigel, Tong Lu, Gilles Bailly, Antti Oulasvirta, Carmel Majidi, Jurgen Steimle (Saarland Univ., Carnegie Mellon Univ., CNRS LTCI, Aalto Univ.) "CTU *OUSPEVDUJPO $PODMVTJPO ਎ମͷλονೖྗ༻ͷൽෘຎ໣ηϯαʔͷ৽͍͠ΫϥεͰ͋ΔJ4LJOΛఏҊɻੜମద߹ੑࡐྉͰ࡞ΒΕͨඇৗʹബ ͍ηϯαʔΦʔόʔϨΠͰɼॊೈͰ৳ॖੑ͕͋Δɻ਎ମͷ༷ʑͳ৔ॴʹద߹͢ΔΑ͏ʹɼҟͳΔܗঢ়ٴͼαΠζͰ ੡଄͢Δ͜ͱ͕Ͱ͖ΔɻϞόΠϧίϯϐϡʔςΟϯάͷͨΊͷɼ௚઀తɼਝ଎͔ͭ৻ॏͳೖྗΛଅਐ͢Δɻ ൽෘ্ʹ௚઀ஔ͘͜ͱ͕Ͱ͖ΔλονηϯαΛઃܭ࣮͠૷͢Δ͜ͱ͸ࠔ೉Ͱ͋ͬͨɻਓؒͷίϯϐϡʔλ্ͰͷਓؒͱίϯϐϡʔλͱͷΠϯ λϥΫγϣϯͷͨΊͷΠϯλʔϑΣʔεͰ͋ΔJ4LJOΛఏҊɻΠϯλϥΫςΟϰͳ໘͸ৗʹར༻ՄೳͰελϯυΞϩʔϯ·ͨ͸ଞͷϞόΠϧσ όΠεͷೖྗαʔϑΣʔεͱͯ͠ૉૣ͘৻ॏʹૢ࡞Ͱ͖Δɻؔ࿈࡞ۀͷϨϏϡʔɼηϯαͷ࣮૷ํ๏ɼΞϓϦέʔγϣϯϨΠͱධՁௐࠪͷ݁ ՌΛࣔ͢ J4LJO͸ɼ߶ମηϯαʔ΍ίϯϐϡʔλϏδϣϯϕʔεͷղܾ๏ʹ༏ΔɻൽෘͷλονೖྗΛαϙʔτ͍ͯ͠Δɻઃܭ͸ɼಛఆ ͷΞϓϦέʔγϣϯ΍ϘσΟͷҐஔɼ๬·͍͠֎؍ɼϘλϯͷϨΠΞ΢τʹର͢ΔλονηϯαͷΧελϚΠζΛαϙʔτ͢ Δɻ͜ΕʹΑΓɼਓࠩ͠ࢦ΍ࣖͷޙΖͷΑ͏ͳඇৗʹ௅ઓతͳ਎ମ෦෼΁ͷΞϓϦέʔγϣϯΛ͍ͤ͘͢͞Δ͜ͱ͕Ͱ͖ͨɻ 
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114. MatchPoint: Spontaneous Spatial Coupling of Body Movement for Touchless Pointing 8,67d Christopher Clarke, Hans Gellersen ʹΞ͵΍͹ʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ٗढ़Ώघ๑͹ΫϠͺʹ͞ʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ٠࿨ͺ͍Ζʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ ਑౪प৷ःॊ੟৬पेॊૻຎؚใோু১धभএॖথॱ୎ ৿ৎ৑भૻຎ॑ষढञ؛ ੟৬ऊैਵखञু॑ෙढथ୯඄घॊਖ਻ऋோ૔घॊञीؚ ୦भ੟৬ऩभऊ॑ੳ௙औचॊ૑ਏऋँॊ؛ Orbits 201713110 ᱎઢၲᆓ #2 (য৑॥ش५) ਐۛͶ͍Ζ෼ରΏਐର͹Ҳ෨ΝྡؽԢรͶϛ΢ ϱν੏ޜͶ༽͏Ζϛ΢ϱτΡϱήघ๑ घΏࢨ͹ಊ͘͹खಚͶϛ΢ϱτΡϱήͲϜʖΩ ΏήϫʖϔɼϪʖδϛ΢ϱνΝචགྷͳ͢͵͏ υΡηϕϪ΢৏ͶݳΗΖνʖΰρφͳಋͣಊ͘ ΝͤΖ෼ରΝΩϟϧͲखಚͤΖ
115. 2UELWV*D]H,QWHUDFWLRQIRU6PDUW:DWFKHVXVLQJ6PRRWK3XUVXLW(\H0RYHPHQWV8,67d Augusto Esteves1 , Eduardo Velloso2 , Andreas Bulling3 , Hans Gellersen4 201713110 ᱎઢၲᆓ #2 (য৑॥ش५) Ϥʖδ͹໪͹ಊ͘ͶΓͮͱɼηϜʖφΤΧρο͹ύϱθϓϨʖ೘ྙΝՆ೵ͶͤΖٗढ़Ͳ͍Ζɽ ͞͹ٗढ़ͺηϜʖφΤΧρο͹ڳ͏ժ໚ͳ࣎ܯͳ͢ͱ͹ࣙષ͵ݡͪ໪ͶଲԢ͢ͱ͏Ζɽ OrbitsΝ༽͏ͪԽֺϕϪ΢Ϣʖɼ௪எϓΥ΢ηϕϪʖφɼ෈ࡑ஥৶ϟωϣʖΝࡠ੣ͪ͢ɽ
116. PathSync: Multi-8VHU*HVWXUDO,QWHUDFWLRQZLWK7RXFKOHVV5K\WKPLF3DWK0LPLFU\&+,d 0DUFXV&DUWHU(GXDUGR9HOORVR-RKQ'RZQV$ELJDLO6HOOHQ.HQWRQ2b+DUD)UDQN9HWHUH 201713110 ᱎઢၲᆓ #2 (য৑॥ش५) ۯ஦Ͳ͹ϜϩοϤʖδͶΓΖζΥηοϡٗढ़Ͳ͍Ζɽ ϨθϝΩϩ͵ϏηΝΉ͸Ζ͞ͳͲࣰݳͤΖɽ ϏϔϨρέτΡηϕϪ΢Ͷ͕͜ΖϜϩοϤʖδΠϕϨίʖεϥϱͶ޴͏ͱ͏Ζɽ
117. 3HUVRQDO6SDFH8VHU'HILQHG*HVWXUH6SDFHIRU*8,,QWHUDFWLRQ&+,d Alvin Jude , G. Michael Poor , Darren Guinness 201713110 ᱎઢၲᆓ #2 (য৑॥ش५) εητϞͶ߻ΚͦͱϤʖδ͗ζΥηοϡΝߨ͑͹Ͳͺ͵͚ɼϤʖδ͗ఈ࣏ٝͪ̑͢ݫۯؔͲζΥη οϡ೘ྙΝߨ͑ݜڂͲ͍ΖɽβϨϧΠʖϞεϱχϫʖϞΝմ݀ͤΖͪΌͶϤʖδͺඞΝطͶ஖͚͞ ͳ͗Ͳ͘ΖɽΩʖλϩ૤ࡠ͹ͪΌ͹ζΥηοϡͳർֳ͢ɼ඲Ճͪ͢ɽ
118. 3RUWLFR7DQJLEOH,QWHUDFWLRQRQDQGDURXQGD7DEOHW8,67d Daniel Avrahami1,2, Jacob O. Wobbrock2 and Shahram Izadi3 201713110 ᱎઢၲᆓ #2 (য৑॥ش५) PorticoͺνϔϪρφ৏͕Γ;बวͲ͹νϱζϔϩ΢ϱνϧέεϥϱΝՆ೵ͶͤΖεητϞͲ͍Ζɽ υΡηϕϪ΢͹৏Ͷ͍Ζ̐ͯ͹ΩϟϧͶΓͮͱ෼ରΝࣟพͤΖɽ
119. MultiPoint: Comparing Laser and Manual Pointing As Remote Input in Large Display Interactions Int. J. Hum.-Comput. Stud. 70 Amartya Banerjee, Jesse Burstyn, Audrey Girouard, and Roel Vertegaal 201713110 ᱎઢၲᆓ #2 (য৑॥ش५) ϪʖδΝ༽͏ͱघΏࢨ͹ಊ͘ΝͳΔ͓ɼԗ๏͹ηέϨʖϱͶԗۛ๑Ν༽͏ͱҒ஖Ν౦ӪͤΖ
120. Full 3D Reconstruction of Transparent Objects Bojian Wu, Yang Zhou, Yiming Qian, Minglun Gong, Hui Huang ʹΞ͵΍͹ʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ٗढ़Ώघ๑͹ΫϠͺʹ͞ʃ ٠࿨ͺ͍Ζʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ಃ໎͵ΨϔζΥέφ͹3࣏ݫ͹ܙয়ΝࣙಊదͶϠυϩԿͤΖघ๑ɽ 201813559 ஓീ ֋౉ #2 (෻αʖη) ࠹ॵͶΨϔζΥέφ͹୉Ή͖͵ܙয়ΝϠυϩԿͪ͢ϧϓϠυϩΝ ࡠ੔͢ɼΨϔζΥέφͳިࠫͤΖޭત͹ଲԢؖܐΝ༽͏ͱɼϧϓ ϠυϩΝ࠹నԿ͢ͱ͏͚ɽ ϠυϩԿͪ͢΍͹ͳɼࣰࡏ͹ΨϔζΥέφͳ͹න໚͹ړ཯͹ޣࠫ Νܯ଎ͪ͢ɽ SIGGRAPH 2018 ॊཔݜڂͲͺಃ໎͵ΨϔζΥέφ͹2ͯ͹໚͹఼܊Νਫ਼੔ͤΖ͞ͳ ͺड़པͪ͗ɼͨ͹ܙয়ͺ෈׮સͫͮͪɽ͞͹घ๑Ͳͺɼ׮સ͵̑D ϠυϩΝ෰ݫͤΖ͞ͳ͗Ͳ͘Ζɽ ஦ۯ͹ಃ໎͵ΨϔζΥέφͺॴཀྵͲ͘͵͏ɽ ΨϔζΥέφͶଲ͢ͱɼޭત͹۸઄͹յ਼͗ଁ͓Ζͳޣࠫ͗୉͘ ͚͵Ζɽ Qian et al. 3D Reconstruction of Transparent Objects with Position-Normal Consistency. [2016]
121. MonoPerfCap: Human Performance Capture from Monocular Video WEIPENG XU, AVISHEK CHATTERJEE, and MICHAEL ZOLLHÖFER, Max Planck Institute for Informatics HELGE RHODIN, EPFL DUSHYANT MEHTA, HANS-PETER SEIDEL, and CHRISTIAN THEOBALT, Max Planck Institute for Informatics ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯ༗ޮͩͱݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δʁ ࣍ʹಡΉ΂͖࿦จ͸ʁ 201813564 ౔԰༏അ #2 (෹ίʔε) SIGGRAPH 2018 ୯؟ΧϝϥͰࡱӨ͞Εͨө૾Λجʹɼਓؒͷϙʔζ͔ Βண༻͢Δҥ෰·ͰΛ3D࠶ߏங͢ΔϚʔΧϨεύ ϑΥʔϚϯεΩϟϓνϟٕज़ͷఏҊ ୯؟ΧϝϥʹΑͬͯࡱӨ͞Εͨө૾ͷΈΛೖྗͱ͠ ͨॳΊͯͷ3DύϑΥʔϚϯεΩϟϓνϟٕज़Ͱ͋ Δɽ Sparse 2Dͱbatchϕʔεͷ࢟੎ਪఆख๏Λ༻͍ͨCNN ʹΑΔਓؒͷ3D࢟੎ݕग़Λ࠾༻͍ͯ͠Δɽ ߹ܭ120ຊͷϏσΦΫϦοϓΛ༻͍ͯϕϯνϚʔΫΛ ߦͬͨɽ࣭తͳݕূͰ͸ɼશͯͷϕϯνϚʔΫσʔ ληοτʹର͠ɼ׬શͳ݁ՌΛಘͨɽ ࠷దԽ͞Ε͍ͯͳ͍CPUίʔυͰ͸ҰͭͷೖྗϑϨʔ ϜΛॲཧ͢Δͷʹ໿1.2෼Λཁ͕ͨ͠ɼdata parallel optimizationΛ༻͍Δ͜ͱͰେ෯ʹվྑͰ͖Δɽকདྷ తʹ͸ϦΞϧλΠϜॲཧΛ໨ࢦ͢ɽ Robertini et al. 2016
122. MonoPerfCap: Human Performance Capture from Monocular Video WEIPENG XU, AVISHEK CHATTERJEE, and MICHAEL ZOLLHÖFER, Max Planck Institute for Informatics HELGE RHODIN, EPFL DUSHYANT MEHTA, HANS-PETER SEIDEL, and CHRISTIAN THEOBALT, Max Planck Institute for Informatics ʹΞ͵΍͹ʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ٗढ़Ώघ๑͹ΫϠͺʹ͞ʃ ٠࿨ͺ͍Ζʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ 201813564 ౖԲ༑ഇ #2 (෻αʖη) SIGGRAPH 2018 ୱءΩϟϧͲࡳӪ͠Ηͪӫ଀ΝخͶɾਕؔ͹ϛʖθ ͖Δ஥༽ͤΖҧෲΉͲΝ3D࠸ߑ஛ͤΖϜʖΩϪηϏ ϓΧʖϜϱηΫϡϕοϡٗढ़͹఑Ҍ ୱءΩϟϧͶΓͮͱࡳӪ͠Ηͪӫ଀͹ΊΝ೘ྙͳ͢ ͪॵΌͱ͹3DϏϓΧʖϜϱηΫϡϕοϡٗढ़Ͳ͍Ζɿ Sparse 2Dͳbatchϗʖη͹ࢡ੐ਬఈघ๑Ν༽͏ͪCNN ͶΓΖਕؔ͹3Dࢡ੐ݗड़Νࡀ༽͢ͱ͏Ζɿ ߻ܯ120ຌ͹ϑυΨέϨρϕΝ༽͏ͱϗϱοϜʖέ Νߨͮͪɿ࣯ద͵ݗৄͲͺɾસͱ͹ϗϱοϜʖέ υʖνιρφͶଲ͢ɾ׮સ͵݃ՎΝಚͪɿ ࠹నԿ͠Ηͱ͏͵͏CPUαʖχͲͺҲͯ͹೘ྙϓ ϪʖϞΝॴཀྵͤΖ͹Ͷ༁1.2෾Νགྷͪ͗͢ɾdata parallel optimizationΝ༽͏Ζ͞ͳͲ୉෱ͶրྒྷͲ͘ ΖɿগཔదͶͺϨΠϩν΢ϞॴཀྵΝ໪ࢨͤɿ Robertini et al. 2016
123. 201611432 Ԯ෨ཀྵँࢢʤ෻αʖηʥ Reference Data for Polysomnography-Measured and Subjective Sleep in Healthy Adults. ॽ͏ͱ͍Ζ಼༲ ݊৙੔ਕ͹ਯເͶؖͤΖυʖνΝͳͮͱ ೧ྺʀ੓พͺਯເͶӪڻͤΖ͹͖ߡࡱͪ͢ ࣏Ͷಣ΋΄͘࿨ช ஦ࠅਕາमָࣉ͹ਯເϏνʖϱͶؖͤΖ ࿨ช͍͗ͮͪ͹ͲɼชԿ͹ҩ͏Ͳਯເ͗ รΚΖ͹͖ɼಣΞͲΊͪ͏ͳ͕΍͏Ή͢ ͪʄ Κ͖ͮͪ͞ͳ ೧ྺ͗߶͚͵ΖͶͯΗͱɼਯເͺΓΕ୻͚ ͵ͮͪɽ Ήͪɼঃ੓͹ਯເͺ஋੓͹ਯເΓΕ਄͏͞ ͳ͗෾͖ͮͪɽ ΍ͮͳਜ਼͢͏υʖνΝಚΖͪΌͶ ୉وໝ͵ࠅࡏదυʖνϗʖη͹ܙସΝͳΖ චགྷ͍͗Ζɽ ͞͹υʖνͫ͜Ͳͺक؏ద͵υʖνʄʄ Ӏ༽ݫʂhttp://jcsm.aasm.org/ViewAbstract.aspx?pid=31232
124. Full 3D Reconstruction of Transparent Objects Bojian Wu, Yang Zhou, Yiming Qian, Minglun Gong, Hui Huang ʹΞ͵΍͹ʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ٗढ़Ώघ๑͹ΫϠͺʹ͞ʃ ٠࿨ͺ͍Ζʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ಃ໎͵ΨϔζΥέφ͹3࣏ݫ͹ܙয়ΝࣙಊదͶϠυϩԿͤΖघ๑ɽ 201813559 ஓീ ֋౉ #2 (෻αʖη) ࠹ॵͶΨϔζΥέφ͹୉Ή͖͵ܙয়ΝϠυϩԿͪ͢ϧϓϠυϩΝ ࡠ੔͢ɼΨϔζΥέφͳިࠫͤΖޭત͹ଲԢؖܐΝ༽͏ͱɼϧϓ ϠυϩΝ࠹నԿ͢ͱ͏͚ɽ ϠυϩԿͪ͢΍͹ͳɼࣰࡏ͹ΨϔζΥέφͳ͹න໚͹ړ཯͹ޣࠫ Νܯ଎ͪ͢ɽ SIGGRAPH 2018 ॊཔݜڂͲͺಃ໎͵ΨϔζΥέφ͹2ͯ͹໚͹఼܊Νਫ਼੔ͤΖ͞ͳ ͺड़པͪ͗ɼͨ͹ܙয়ͺ෈׮સͫͮͪɽ͞͹घ๑Ͳͺɼ׮સ͵̑D ϠυϩΝ෰ݫͤΖ͞ͳ͗Ͳ͘Ζɽ ஦ۯ͹ಃ໎͵ΨϔζΥέφͺॴཀྵͲ͘͵͏ɽ ΨϔζΥέφͶଲ͢ͱɼޭત͹۸઄͹յ਼͗ଁ͓Ζͳޣࠫ͗୉͘ ͚͵Ζɽ Qian et al. 3D Reconstruction of Transparent Objects with Position-Normal Consistency. [2016]
125. Silicone Devices: A Scalable DIY Approach for Fabricating Self-Contained Multi-Layered Soft Circuits using Microfluidics 201611447 ੜ༐ื #2 (෻αʖη) Steven Nagels , Raf Ramakers , Kris Luyten , Wim Deferme ʹΞ͵΍͹ʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ٗढ़Ώघ๑͹ΫϠͺʹ͞ʃ ٠࿨ͺ͍Ζʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ ৵क़੓͹͍Ζಢཱིͪ͢ιϱγΝࡠΖघ๑Ώɼ৵क़੓ો ࡒͶుࢢ෨඾ΝດΌࠒ΋घ๑ͺଚࡑͪ͗͢ɼ෵ࡸ͵յ ࿑΃͹Ԣ༽ͺͲ͘͵͖ͮͪɽຌघ๑Ͳͺଡ૜յ࿑΃Ԣ ༽Ͳ͘ΖͪΌɼࣙހ׮݃ܗ͹৵क़੓εϨαϱυώ΢η ࣰ͗ݳͲ͘Ζ յ࿑͹φϪʖηͶӹରۜ଒Ν༽͏Ζ͞ͳͲɼ৵क़੓ͳ ༢಍੓Νಋ࣎Ͷࣰݳͪ͢ɽ εϨαϱΝ༽͏ͱɼ৵क़੓ͳौೊ੓Νඍ͓ͪଡ૜յ࿑ ΝDIYͤΖ๏๑͹఑Ҍ ༢ు੓ͳࣙހյ෰੓Ν඲ՃͤΖͪΌͶɼ৵क़յ਼ͳ৵ क़ྖɼͨΗͶͳ΍͵͑ఏ߇஍͹รԿΝ؏ࡱͪ͢ɽ ಍ు੓ϏρχΝ౹߻ͤΖͫ͜͹΍͹ͳർ΄Ζͳ޲͚͵ Ζ͗ɼമ͏෨඾Ν࢘͑͵ʹͤΗͻ޲͠Ν࠹నԿͲ͘Ζɽ ࡠۂஊ֌͗ଡ͚͖͖࣎ؔ͗Ζ͹Ͳɼٗढ़͗චགྷɽ Michael Wessely, Theophanis Tsandilas, and Wendy E. Mackay. Stretchis: Fabricating Highly Stretchable User Interfaces.
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127. ʹΞ͵΍͹ʃ ઎ߨݜڂͳർ΄ͱʹ͗ͤ͟͞͏ʃ ʹ͑Ώͮͱ༙ްͫͳݗৄͪ͢ʃ ٠࿨ͺ͍Ζʃ ࣏Ͷಣ΋΄͘࿨ชͺʃ ٗढ़͹घ๑ΏΫϠͺʹ͞ʃ ಊ෼Ώਕؔ͹໡͹ϪϱξϨϱήΝਜ਼͖֮ͯްིదͶ ߨ͑घ๑ ໡͹ϪϱξϨϱήͺ[Yan et al. (2005)]͵ʹ͹Ϡυ ϩ͍͗Ζ͗ɼ؈ྲྀԿ͠Η͙ͤͱ͏ͪΕɼ෵ࡸ͙ͤͪ Εۅ୼ͫͮͪɽ͞͹࿨ชͲͺϠυϩ͹؈ྲྀԿΝߨ͏ ͯͯਜ਼֮͵ϪϱξϨϱήΝࣰݳ ܯࢋΝकགྷ͵5ߴͶߞΕɼേ͹஦ৼ෨Ͳ͹ൕ ࣻʀ۸઄Ν঴ྲྀͯͯ͢΍ࢆཛྷޭ͹ٷफްՎͺ ࢔ͤɽԗړ཯ඵժͲ͹࠹నԿͶ΍ଲԢ ֱౕ͟ͳ͹ൕࣻΝεϝϣϪʖεϥϱ͢[Yan et al.(2005)]͹ࣰ଎υʖνͳεϝϣϪʖεϥϱ ͳޣࠫΝർֳɽϪϱξϨϱή݃ՎͲ͹ർֳ ؈ྲྀԿͶΓͮͱऑױ੠ౕ͗ଝ͵ΚΗΖ͗֕͸ ྒྷ޹ɽਕ͹േͺ઎ߨݜڂͲ΍͞͹࿨ชͲ΍࠸ ݳ͗ೋ͢͏ɽ Yan et al. (2015), Marschner et al. (2003) James Kajiya and Timothy Kay. (1989) 201511455 ௗԮಠ࢛ #1 (෻αʖη) ACM Transactions on Graphics, Vol. 36, No. 4, Article 67. Publication date: July 2017. SIGGRAPH 2017
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130. ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯ ٕज़ͷΩϞ͸ʁ ٞ࿦ ࣍ʹಡΉ࿦จ ҏ౻ ճં֨ࢠૉࢠΛ༻͍ͨIPMPHSBQIJD
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131. ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯ ٕज़ͷΩϞ͸ʁ ٞ࿦ ࣍ʹಡΉ࿦จ ҏ౻ 
 
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 ͜ͷݚڀͰ͸σΟεϓϨΠͱηϯγϯάʹಉ͡ϨϯζΞϨΠ Λ༻͍ͨ ׯবΛى͜͞ͳ͍ͨΊʹ੺֎ઢΧϝϥ*3ϖϯΛ༻͍ͯ ηϯγϯά͸੺֎ઢྖҬͰߦͬͨ
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 ཭ΕΔͱ៉ྷʹݟ͑Δ͕ɼͦΕͰ͸ηϯγϯάͷ ਫ਼౓͕Լ͕Δ !2 6*45`
132. ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯ ٕज़ͷΩϞ͸ʁ ٞ࿦ ࣍ʹಡΉ࿦จ ҏ౻ 
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133. ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯ ٕज़ͷΩϞ͸ʁ ٞ࿦ ࣍ʹಡΉ࿦จ ҏ౻ 
 
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134. ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯ ٕज़ͷΩϞ͸ʁ ٞ࿦ ࣍ʹಡΉ࿦จ ҏ౻ 
 
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135. ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯ ٕज़ͷΩϞ͸ʁ ٞ࿦ ࣍ʹಡΉ࿦จ ҏ౻ 
 
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 ৭ऑͷλΠϓ͕ಉ͕ͩ͡ɼॏେ౓͕ҟͳΔ৔߹ɼ࠷΋ॏେͳਓ ʹ߹ΘͤΔඞཁ͕͋Δ
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136. ͲΜͳ΋ͷʁ Ͳ͏΍ͬͯݕূͨ͠ʁ ઌߦݚڀͱൺ΂ͯ ٕज़ͷΩϞ͸ʁ ٞ࿦ ࣍ʹಡΉ࿦จ ҏ౻ 
 
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     Λ࡞ͬͯ΋Β͏λεΫߦ͍ɺ࣌ؒΛܭଌͨ͠ɻ ճస࣠ʹج͍ͮͨػցʹݶఆ͞Ε͍ͯΔɻ 4ZNNFUSZ
     JO
     E
     HFPNFUSZ
     &YUSBDUJPO
     BOE
     BQQMJDBUJPOT
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     $PNQVUFS
     (SBQIJDT
     'PSVN
     7PM
     
     /P
     
     QQ
      
     'VODUJPOBMJUZBXBSF
     SFUBSHFUJOH
     PG
     NFDIBOJTNT
     UP
     %
     TIBQFT
     50( ػߏͷύʔπΛάϥϑߏ଄Ͱॲཧɺ3"/4"$ΞϧΰϦζϜʹΑΔճ స࣠ͷݕग़ͳͲͰɺύʔπಉ࢜Λͭͳ͙͜ͱ͕Ͱ͖Δ $)*
165. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ϑΟδΧϧ6*Λࣗ༝ͳܗঢ়ͷฏ໘্Ͱ(6*ͰϨΠΞ΢τ͠ɺ༗ܗͷ΢Ο δΣοτ 6*ίϯϙʔωϯτ Λ಺ଆʹӅΕΔΑ͏ʹ഑ஔͳͲΛௐ੔͢ Δπʔϧ ࠷ऴతͳϝογϡͷεϜʔδϯάͷख๏͸ɺ෺ཧతͳγϛϡϨʔγϣ ϯͰ͸ͳ͘ɺՄࢹੑʹج͍͍ͮͯΔͨΊ༰қͳ఺ ࢖͍΍͢͞ͷݕূͷͨΊʹඃݧऀ࣮ݧΛ͠ɺλεΫ࣌ؒͱ໨ඪઃܭͱ ͷζϨΛܭଌͨ͠ɻ ۂ཰ͷมԽɺ·ͨ͸ɺϒϥέοτͷߏ଄ʹΑͬͯ෦඼͕ΤϯΫϩʔδϟ ʹΑͬͯӅΕͳ͍͜ͱ 
     "
     4FSJFT
     PG
     5VCFT
     "EEJOH
     *OUFSBDUJWJUZ
     UP
     %
     1SJOUT
     6TJOH
     *OUFSOBM
     1JQFT
     6*45 ύʔπͷͨΊͷ࿐ग़ͨ͠ϒϥέοτΛΤϯΫϩʔδϟͷ಺ଆʹ׬શʹ ऩΊΔΑ͏ʹϒϥέοτΛௐ੔͢ΔΞϧΰϦζϜ $)*
166. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε γϦίϯͷܕͱҨ఻త഑ஔΞϧΰϦζϜΛ࢖༻ͯ͠Ϩγϐͷ͞·͟· ͳܗঢ়ஔ׵Λ࣮ݱ͠ɺྉཧͷຯ֮ߏ଄Λ੍ޚͰ͖Δ΋ͷ %*(*5"-
     ("4530/0.: 6*45ʟ Ͱ͸ɺ৯΂෺ͷܗ͚ͩΛมԽ ͕ͤͨ͞ɺ৯΂෺ͷຯ΋ϓϩάϥϜՄೳʹͨ͠ ৭ϜʔεΛ࡞ͬͯΈͨɻΞʔϞϯυγϡτϩΠθϧɺΠνΰϜʔεɺ μʔΫνϣίϨʔτϜʔεɺϗϫΠτνϣίϨʔτϜʔε %৯඼ҹ࡮ͷओͳݶքͷͭ͸ɺੜͷ৯෺ࡐྉͱϑϨʔόʔͷछྨ ͕গͳ͍͜ͱͰ͢ɻ
     
     
     
     ৯΂෺ʹΑͬͯ଄ܗ͕஗͍ 'PSN
     BOE
     $PEF
     *O
     %FTJHO
     "SU
     BOE
     "SDIJUFDUVSF
     "
     (VJEF
     UP
     $PNQVUBUJPOBM
     "FTUIFUJDT OPU
     QBQFS
     1BSBNFUSJD
     #VJMEJOH
     %FTJHO
     6TJOH
     "VUPEFTL
     .BZB
     3PVUMFEHF OPU
     QBQFS ྉཧͷཁ݅ΛϏοτͷ഑ஔͱྉཧͷྔ
     ܗঢ়ͷ܁Γฦ͠ɺόϥϯεɺ Ϗοτ෼഑༏ઌ౓ΛύϥϝʔλΛ΋ͭ໨తؔ਺Λ࢖͍Ҩ఻తΞϧΰϦ ζϜʹΑͬͯ࠷దԽ໰୊Λղ͍͍ͯΔɻ $)*
167. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε '%.ํࣜΛ༻͍ͨંΓͨͨΈʹΑΔ%ܗঢ়ͷϥϐουϓϩτλΠϐ ϯάख๏ ۂ໘ંΓࢴ ࣗݾંࠐܗঢ়ͷҹ࡮͸ɺߴ͍අ༻ͷ͔͔ͬͨ%ϓϦϯλ΍ಠࣗʹ࡞ ੒ͨ͠ϓϦϯλ͕ඞཁ͕ͩͬͨɺ͜Ε͸ࢢൢͷ'%.ํࣜϓϦϯλͰ Մೳͳ఺ ͜ͷख๏Λ༻͍ͯΞϓϦέʔγϣϯΛ࡞੒ͨ͠ɻ"SU
     
     4FMGGPMEJOH
     3PTF
     5SBOTQPSUBUJPO
     
     4FMG'PMEJOH
     #PBU
     'VSOJUVSF
     
     4FMG 'PMEJOH
     $IBJS
     8FBSBCMFT
     
     4FMG'PMEJOH
     "SNPS ग़ྗϓϦϯλʹΑΔղ૾౓ͷ੍ݶɺॏྗͱࡐྉͷࣗॏ͕ܭࢉͰ͖ͳ͍ ͷͰɺ͋Δఔ౓ߏ଄ʹ੍ݶ͕͋Δɻώϯδͷܺؒʹ͕݀͋Δ %
     1SJOUJOH
     BOE
     VOJWFSTBM
     USBOTGPSNBUJPO
     "$"%*" 5IFSNP
     QPMZVSFUIBOF ˆͰ༥ղ͠ɺˆҎ্ͰTFMGGPMEJOH
     FGGFDUTΛ࣋ͭ ͷҹ࡮Λɺۂ͕Δ֯౓ʹΑͬͯɺҹ࡮଎౓ͱ௕͞ͷௐ ੔Λͨ͠ɻ $)*
168. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε '%.ํࣜͷϓϦϯλͰࢴͷ্ͷʹΞΫνϡΤʔλͱͯ͠࡞༻͢Δಋ ిੑϑΟϥϝϯτΛҹ࡮ͨ͠ϖʔύʔΞΫνϡΤʔλ 'PMEJPͱ͸ҧ͏఺͸ɺ'%.ํࣜΛ༻͍ͯҹ࡮ͨ͠ύλʔϯ͕఍߅Ճ ೤ཁૉΛ͔࣋ͬͯͭΞΫνϡΤʔλͱͯ͠ಇ͘͜ͱ ͜ͷख๏Λ༻͍ͨΞϓϦέʔγϣϯΛ࡞੒ͨ͠ɻ·ͨɺΞΫνϡΤʔ λͱͯ͠ͷԠ౴࣌ؒΛܭଌͨ͠ɻ
     ͜ͷख๏Ͱ͸ඵͰ׬શʹ΋ͱʹ΋ͲΔ (SBQIFOF
     
     1-"͕๲ு͢ΔͨΊϊζϧ͕٧·Δɻϊζϧͷߴ͞Λௐ ੔͠ͳ͚Ε͹ࣦഊ͢Δɻൺֱతߴ͍ిѹ 7 Λඞཁͱ͢Δ 'PMEJP
     %JHJUBM
     GBCSJDBUJPO
     PG
     JOUFSBDUJWF
     BOE
     TIBQFDIBOHJOH
     PCKFDUT
     XJUI
     GPMEBCMF
     QSJOUFE
     FMFDUSPOJDT
     6*45 (SBQIFOF
     1-"Λ༻͍ɺిؾత఍߅Ճ೤ʹΑͬͯܗঢ়͕Ͳͷఔ౓· ͕Δ͔ΛܭଌɾԠ༻͠σβΠϯπʔϧͱͯ͠ఏڙͨ͠఺ͱɺ $)*
169. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε %ϓϦϯλΛ༻͍ͨిؾΛඞཁͱ͠ͳ͍ճݶΓͷ
     ΦϑϥΠϯύογϒηϯα
     ෛՙɺѹྗɺՃ଎ɺ܏ࣼɺԹ౓มԽΛײ஌͢Δ Λ࡞੒͢Δख๏ ిࢠ෦඼Λ௥ՃͰඞཁͱͤͣɺ%ϓϦϯλͷΈͰ׬݁͢Δ఺ ࡞੒ͨ͠ηϯαΛ༻͍ɺ
     ηϯα͕ಈ࡞͢Δఆྔతͳ෺ཧྔΛ༩͑ݕূͨ͠ɻ ݀ͷ଄ܗ͸%ϓϦϯλʹΑΔɺ·ͨزԿֶత੍໿ ߴ͍ۂ཰ɺബ͍γΣ ϧͳͲ͸೉͍͠ ͕͋Γɺճ͔͠࢖͑ͳ͍ .FUB4JMJDPOF
     %FTJHO
     BOE
     'BCSJDBUJPO
     PG
     $PNQPTJUF
     4JMJDPOF
     XJUI
     %FTJSFE
     .FDIBOJDBM
     1SPQFSUJFT 50( ηϯαͷ಺ଆʹ͸ಋిੑͷഔମ ࿦จதͰ͸ਫಓਫ Λ༻͍ͯɺ੩ి༰ ྔλονύωϧͰɺηϯαͷ5SVF
     PS
     'BMTFΛݕग़͍ͯ͠Δ఺ $)*
170. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε डಈతͳ෺ମʹ༰қʹద༻Ͱ͖ΔιϑτΞΫνϡΤʔλͱɺͦͷΞΫ νϡΤʔλͷͨΊͷ҆ՁͳࡐྉΛ༻͍ͨߴ଎੡଄ํ๏ͷ։ൃ ػցతཁૉ ػց෦඼ Λ·ͬͨ͘औΓ෇͚Δ͜ͱ͕ແ͍఺
     ത෺ؗͰϫʔΫγϣοϓ ໊෼ Λ։͖͜Ͳ΋ʹɺ༡ΜͰ΋Β͍ ؆୯ͳΞϯέʔτʹ౴͑ͯ΋Βͬͨɻ
     ࢖༻཰͕ΞΫνϡΤʔλͱͯ͠ར༻͞Εͨ όΠφϦ੍ޚ͔͠Ͱ͖ͳ͍ɻΞΫνϡΤʔλͱͯ͠ͷ৴པੑͷ؍఺Ͱɺ ର৅Λଛইͤ͞ΔՄೳੑ͕͋ΔͷͱɺΞΫνϡΤʔλࣗମ͕ർ࿑ͯ͠ ͠·͏఺ 1*/0,:
     B
     SJOH
     UIBU
     BOJNBUFT
     ZPVS
     QMVTI
     UPZT $)* Ϣʔβʔ͕೚ҙͷ৔ॴͰ੾ΕΔܗঢ়ͷςʔϓঢ়ͷΞΫνϡΤʔλΛ࡞ ΓγϦϯδʹΑΔ੍ޚख๏Λཱ֬ͨ͜͠ͱ 5&*
171. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ৯༻·ͨ͸ҩֶ༻్ͷͨΊͷɺ৯΂ΕΔ࠶ؼੑ൓ࣹ ৯΂ΕΔ MVNJOBUJPO
     NFUFS -.
     "4
     0/& Λ༻͍ͯ࠶ؼੑ൓ࣹͷಛੑ Λௐ΂ͨɻεΠεϩʔϧʹಈతϓϩδΣΫγϣϯϚοϐϯάΛߦ͍࠶ ؼੑ൓ࣹϚʔΧʔͱͯ͠ͷ༗༻ੑΛࣔͨ͠ɻ פఱʹؚ·ΕΔਫͷৠൃʹΑΓ࣌ؒͷܦաͱͱ΋ʹɺ࠶ؼ൓ࣹػೳΛ ࣦ͏͜ͱɻ·ͨɺߴԹͰͷݕূΛ͍ͯ͠ͳ͍ɻೖࣹ֯ਖ਼໘౓Ҏ಺ Ͱ͔͠࠶ؼੑ൓ࣹ͠ͳ͍ɻ &EJCMF
     PQUJDT
     6TJOH
     HFMBUJO
     UP
     EFNPOTUSBUF
     QSPQFSUJFT
     PG
     MJHIU
     OPU
     QBQFS
     5IF
     1IZTJDT
     5FBDIFS
     
      
     &EJCMF
     -BTFST
     8IBUʟT
     UIF
     /FYU
     $PVSTF
     
     OPU
     QBQFS
     01/
     .BZ
      ಁ໌ͷ৯ࡐͰͷ۶ં཰͕Α͍פఱΛ࢖ͬͯίʔφʔΩϡʔϒΞϨΠΛ ͭͬͨ͘ 7345
172. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε +BNNJOHݱ৅Λར༻ͨ͠มܗՄೳͰ߶ੑΛௐ੔Մೳͳബ͍γʔτ 1BSUJDMF
     +BNNJOHͱൺֱͯ͠ɺΠϯλϑΣʔεΛബܕɺܰྔʹߏங ͢Δ͜ͱ͕Ͱ͖Δ ಛ௃ ߶ੑௐ੔ੑɺࡐྉڧ౓ɺܰྔԽ ΛධՁ͢ΔͨΊͷͭͷΞϓϦ έʔγϣϯΛͭͬͨ͘ ๲ுͷͨΊͷϙϯϓͱਅۭҾ͖ͷͨΊͷϙϯϓ͕ඞཁͰɺͦΕΛར༻ ͨ͠ΞϓϦέʔγϣϯ͸͔ͭ͠ͳ͘ɺݕূ͕ݶఆతͳ఺ +BNNJOH
     VTFS
     JOUFSGBDFT
     QSPHSBNNBCMF
     QBSUJDMF
     TUJGGOFTT
     BOE
     TFOTJOH
     GPS
     NBMMFBCMF
     BOE
     TIBQFDIBOHJOH
     EFWJDFT 6*45
     $BQBCJMJUZ
     CZ
     -BZFS
     +BNNJOH
     *304 +BNNJOHݱ৅ΛҾ͖ى͜͢ߏ଄Λ-BZFSঢ়ʹͨ͠఺ɻ 5&*
173. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ӷମʹΑͬͯ೚ҙͷ࣍ݩܗঢ়ʹల։͞ΕΔ%Մ৯ϑΟϧϜ ৯΂Ε͔ͯͭɺมܗલ͕ӡૹʹ഑ྀͨ͠ฏ໘Ͱ͋Δ఺ ܗঢ়ม׵ʹΑͬͯ৯෺ࡐྉͷแΈࠐΈɺܗঢ়ɺ࣭ײͱ૬ޓ࡞༻Λୡ੒ ͢ΔΧελϚΠζੑΛ࣮ূ͢ΔͭͷϝχϡʔΛࢼͨ͠ɻ ࣗ࡞ϓϦϯλͰ͋Γɺ·ͩࢢൢͷࡐྉΛར༻ͨ͠มܗՄ৯ϑΟϧϜΛ ͍ͭͬͯ͘ͳ͍ (BTUSPOPNZ
     .FUIPET
     
     3FDJQFT
     GPS
     )ZCSJE
     $PPLJOH
     6*45 ηϧϩʔεણҡͷ߶ੑʹΑΔਫ࿨ʹΑͬͯۂ͛Λ੍ޚ͍ͯ͠Δ
     ࡐྉྗֶͱزԿֶΛ΋ͱʹ୯७Խ͞Εͨ༗ݶཁૉղੳγϛϡϨʔγϣ ϯͷϓϥοτϑΥʔϜΛ࡞੒ͨ͠ɻ $)*
174. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ͋ΔύλʔϯΛଞͷύλʔϯʹม׵͢Δ%ϙοϓΞοϓͷϝΧχζ ϜΛࣗಈతʹੜ੒͢Δ΋ͷ %Ͱ͸͋Δ͕"UP#ͷ೚ҙܗঢ়ͷมܗΛ%Λ௨ͯ͠ߦ͍͑ͯΔɻ ͞·͟·ͳมܗύλʔϯΛࢼ͠ɺ৑௕ྖҬ ຊདྷແ͍ྖҬ ͷग़ݱΛ؍ ࡯ɻ·ͨߟҊͨ͠ΞϧΰϦζϜͷదԠճ਺ͱॲཧ࣌ؒΛܭଌͨ͠ɻ ࢴΛޮ཰తʹ࢖͏͜ͱΛߟ͍͑ͯͳ͍఺ɺࢴͷް͞ΛͱԾఆ͍ͯ͠ ΔͷͰɺࢴͷྔ͕ଟ͍ͱώϯδ෇͕ۙް͘ͳͬͯ͠·͏఺ #PYFMJ[BUJPO
     'PMEJOH
     %
     0CKFDUT
     *OUP
     #PYFT
     4*((3"1) 
 %FTJHO
     PG
     .BDIJOFSZ
     UI
     FE
     .D(SBXr)JMM
     &EVDBUJPO
     OPU
     QBQFS
     .BSDI
     
       Ϧϯέʔδ΍ػߏʹؔͯ͠·ͱΊɺάϥϑʹج͖ͮܭࢉͨ͠఺ 4*((3"1)
175. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε αϙʔτࡐΛ࢖༻͠ͳ͍%ϞσϧΛ'%.ํࣜͰҹ࡮Λ͢Δ%0'ϩ ϘοτΞʔϜγεςϜ αϙʔτࡐΛඞཁͳ͍Α͏ʹ%ܗঢ়Λมܗͤ͞Δ͜ͱͳ͘ɺೖྗ͠ ͨ%ϞσϧΛαϙʔτࡐແ͠Ͱҹ࡮Ͱ͖Δ఺ ͞·͟·ͳϞσϧΛαϙʔτࡐແ͠Ͱҹ࡮͢Δ͜ͱͰɺγεςϜΛݕ ূͨ͠ɻ·ͨɺ઀ଓ෦ͷڧ౓Λଌఆͨ͠ɻ ҹ࡮଎౓͕ਫ਼౓ͷҡ࣋ͷͨΊʹ஗͍ɻαΠζ͕ϑϨʔϜʹΑ੍ͬͯݶ ͞Ε͍ͯΔɻϩϘοτΞʔϜͷίετ͕ߴ͍ɻෳࡶͳܗঢ়ͷ৔߹ɺ෼ ׂΞϧΰϦζϜ͕ࣦഊ͢Δɻ 4VQQPSU
     TMJNNJOH
     GPS
     TJOHMF
     NBUFSJBM
     CBTFE
     BEEJUJWF
     NBOVGBDUVSJOH
     $"%
     WPM
     
     QQ
     r
     
     %JSFDU
     DPNQVUBUJPO
     PG
     NJOJNBM
     SPUBUJPO
     GPS
     TVQQPSU
     TMJNNJOH $"4& ϩϘοτΞʔϜɺ(DPEFɺ304Λۦ࢖͠γεςϜΛߏங͠ɺϩϘο τΞʔϜͷ֯౓͸ҠಈൣғΛαϯϓϦϯά͢Δ͜ͱͰܾఆɺ'%.ํ ࣜͰͷϏϧυϓϥοτϑΥʔϜΛίϯτϩʔϧͰ͖ΔΑ͏ʹͨ͠఺ *$3"4*(
176. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε %ΦϒδΣΫτΛཱํମ·ͨ͸ശʹม׵͢Δํ๏ ΦϒδΣΫτΛ੾Γ཭ͨ͠ܗঢ়ʹ͢Δ͜ͱͳ͍ɺંΓͨͨΈՄೳͳ୯ Ұͷߏ଄Ͱ͋Δ఺ ࡞੒ͨ͠γεςϜΛ࢖ͬͯɺંΓͨͨΈՄೳͳόχʔɺࢠೣɺं
     υϥΰϯɺ৅Λ࡞੒ͨ͠ɻ δϣΠϯτ͕࠷దͳߏ଄ ΑΓλΠτʹ͍ͨ͠ Ͱ͸ͳ͍ɻΦϒδΣΫ τͷಛ௃Λଚॏ ಛ௃తύʔπΛηάϝϯτԽ͢Δ ͠ͳ͍ $IPQQFS
     1BSUJUJPOJOH
     NPEFMT
     JOUP
     %QSJOUBCMF
     QBSUT 50(
     $POWFSUJOH
     %
     GVSOJUVSF
     NPEFMT
     UP
     GBCSJDBUBCMF
     QBSUT
     BOE
     DPOOFDUPST 50( ϘΫηϧԽɺزԿֶత৘ใʹΑΔؔઅҐஔΛఆٛɺπϦʔ୳ࡧͱ͍͏ ϓϩηεΛ௨ͯ͠ɺͻͱͭͳ͕Γͷܗঢ়Λੜ੒͢Δ఺ 4*((3"1)
177. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε มܗՄೳͳϞσϧͱมܗ͞Εͨϙʔζू߹Λ΋ͱʹɺ
     ͳΔ΂͘ϙʔζ௨Γʹมܗ͢Δҹ࡮ՄೳͳSPE
     NFTIΛੜ੒͢Δ 3PCϞσϧͷ࠷దԽؔ਺ΛGBCʹར༻ͨ͠఺ ఏҊ͢Δख๏Λ༻͍ͯγʔτ΍)BUɺڪཽɺεϚΠϧΛ3PE
     .FTIԽ ͠ɺϕϯνϚʔΫΛߦͳͬͨɻ λʔήοτͷϙʔζ͕෺ཧతʹ
     ΋ͬͱ΋Β͍͠΋ͷͰͳ͚Ε͹ͳΒͳ͍ %JTDSFUF
     FMBTUJD
     SPET 4*((3"1)
     %JTDSFUF
     WJTDPVT
     UISFBET 4*((3"1) ࿡֯ܗͷΤοδͷมܗΛௐ੔͠ɺ
     ੍໿Λຬͨ͢໨తؔ਺ͷޯ഑Λܭࢉͨ͠఺ 4*((3"1)
178. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε αϙʔτࡐ͕ͳ͘ܧ͗໨͕ͳ͍ɺதۭͷόϥϯεͷऔΕͨ%ΦϒδΣ ΫτΛੜ੒͢Δख๏ NBLF
     JU
     TUBOEͱൺ΂ΔͱαϙʔτࡐΛ׬શʹඞཁͱ͠ͳ͍ҹ࡮ͷͨ ΊɺϞσϧΛ෼ׂ͢Δܧ͗໨ͳͲ͕ແ͍ ख๏Λ༻͍ͯɺ͞·͟·ͳϞσϧΛҹ࡮͠NBLF
     JU
     TUBOEͷϞσϧͱ ൺֱͨ͠ ڧ౓ʹ͍ͭͯٞ࿦Λߦͳ͍ͬͯͳ͍఺ɺ·ͨϘΫηϥΠθʔγϣϯʹ Αͬͯදݱग़དྷΔϞσϧ੍͕ݶ͞ΕΔ .BLF
     JU
     TUBOE
     CBMBODJOH
     TIBQFT
     GPS
     %
     GBCSJDBUJPO 50( -BZFSΛ্͔ΒԼʹεΩϟϯ͠ɺΦʔόʔϋϯά֯౓͕Ұఆ৚݅Λຬ ͢Α͏ʹௐ੔Λߦ͏
     $"%(SBQIJDT
     
179. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ೚ҙͷ࣍ݩܗঢ়ʹࣗݾల։͢Δ࣍ݩฏ໘γʔτΛ࡞Δख๏ %ܗঢ়Λܗ੒͢Δͱ͖֎෦ΤωϧΪʔΛ༩͑Δ͜ͱͳࣗ͘ݾͷΤω ϧΪʔͰಈ͘ ࣮ࡍʹख๏Λ༻͍ͯϞσϧΛ࡞ͬͨɻ·ͨɺݻఆύϥϝʔλ΍࠷దԽ ͷύϑΥʔϚϯεɺਖ਼֬ੑʹ͍ͭͯண໨ͨ͠ ෺ཧత৳ॖͷݶք͔Β͢΂ͯͷܗঢ়͕୯Ұͷฏ໘ʹͳΔΘ͚Ͱ͸ͳ͍
     ॏྗΛߟྀ͍ͯ͠ͳ͍ BFSP.PSQI)FBUTFBMJOH
     *OqBUBCMF
     4IBQFDIBOHF
     .BUFSJBMT
     GPS
     *OUFSBDUJPO
     %FTJHO 6*45 ೚ҙ௕͞ͷࡾ֯ܗʹ࠶ϝογϡԽ͠ɺ༗ݶཁૉ๏ʹΑΔόωϞσϧΛ ༻͍ͯɺ࣍ݩܗঢ়Λ࣍ݩʹల։ͨ͠γΣϧͱͦͷγΣϧͷ྆໘Λ ຕͷ஄ੑγʔτΛషͬͨ఺ɻ 4*((3"1)
180. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ฏ໘ͷࡐྉ͔Βബ͍ΒͤΜͷ੾அʹΑͬͯೖྗΦϒδΣΫτʹྨࣅ͠ ͨ%όωͷܗΛ࡞Δख๏ %ฏ໘͔ΒΧοτͨ͠όωܗঢ়Λ௚઀্࣋ͪ͛ͯख࡞ۀͰͷ૊ཱΛ ΑΓ؆୯͔ͭਝ଎ʹߦ͑ΔΑ͏ʹͨ͠ DNن໛ͷ΋ͷΛ࣮ࡍʹ࡞੒ͨ͠Γɺख๏Λ༻͍ͯϞσϧΛ࡞੒͢ Δ͜ͱͰɺύϑΥʔϚϯεɺόωͷ਺ɺޯ഑ɺྨࣅੑɺ׈Β͔͞ʹͭ ͍ͯண໨ͨ͠ ޻࡞ػցͷੑೳ্ɺ
     ۚଐ੡ͷόωͰ͜ͷܗঢ়Λ࡞Δ͜ͱ͕ग़དྷͳ͍͜ͱ (FPEFTJD
     QBUUFSOT 50( ৳ॖ͢Δԁਲ਼ʹͳΔྖҬʹ෼ׂ͠[࣠ํ޲ͷ͹Ͷ࣠ͷ޲͖Λ࠷దԽɺ ࠷దԽ໰୊Λղ͖ܗঢ়Λௐ੔ɺද໘ͷ[࣠Λݻఆ͠ɺYͱZͷ࠲ඪͷҠ ಈͷΈͰόωʹల։Ͱ͖Δܗঢ়Λੜ੒͢Δ &630(3"1)*$4
181. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε Ԡྗʹ଱͑ΕΔ࠷খॏྔͷ%ߏ଄Λੜ੒͢Δख๏ Ͳͷܗʹ΋దԠͨ͠ͱ͖ʹੜ͡ΔܭࢉίετΛ࡟ݮͨ͠
     طଘख๏ΑΓ΋தۭʹߋʹܰྔԽͨ͠ طଘख๏ͱͷൺֱɺϞσϧͷಛ௃ γΣϧͷް͞ɺΞϧΰϦζϜͷऩ ଋੑ ΁ͷண໨ɺ෺ཧతͳϕϯνϚʔΫ ϞσϧͷγΣϧͷް͞Λ࠷దԽ͍ͯ͠ͳ͍఺ɺ
     খ͞ͳಥى͕ͨ͘͞Μ͋ΔΑ͏ͳܗঢ়Ͱ͸ɺ
     ԠྗΛਖ਼֬ʹۙࣅͰ͖ͳ͍ ࠷େԠྗܾఆͷͨΊͷߏ଄࠷దԽ໰୊ͷ'&"ධՁΛ΋ͱʹͨ͠ྟքॠ ؒղੳͱɺͦΕʹԠͨ͡ࡐྉ෼෍ͷઃܭ 4USFTTDPOTUSBJOFE
     UPQPMPHZ
     PQUJNJ[BUJPO
     XJUI
     EFTJHO EFQFOEFOU
     MPBEJOH OPU
     QBQFS
     #VJMEUPMBTU
     4USFOHUI
     UP
     8FJHIU
     %
     1SJOUFE
     0CKFDUT 50( 50(
182. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ࣗ༝ܗঢ়ͰΠϯλϥΫςΟϒʹָثΛ࡞Δ͜ͱ͕
     ՄೳͳγεςϜͷఏҊ ΠϯλϥΫςΟϒʹָثΛ࡞ΕΔΑ͏ʹɺԻڹ఻ୡγϛϡϨʔγϣϯ ͷख๏Λม͑ͯߴ଎Խͨ͠఺ ࣮ࡍʹ࡞੒͠ɺγϛϡϨʔγϣϯͱͷҧ͍Λݕূͨ͠ɻ·ͨɺͲͷα ΠζͰ΋దԠͰ͖Δ͔Λݕূ͢ΔͨΊʹେ͖͍ϞσϧΛࢼͨ͠ɻ ΦΫλʔϒΛ௒͑Δप೾਺ྖҬΛୡ੒͢Δ͜ͱ͸ɺٿܗͷڞৼثʹ ͱͬͯ͸ࠔ೉Ͱ͋Δ఺ɺ
     Ի࣭ଟগ٘ਜ਼ Ի৭ΛγϛϡϨʔγϣϯ͍ͯ͠ͳ͍ ʹ͍ͯ͠Δ఺ *OUFSBDUJWF
     BDPVTUJD
     USBOTGFS
     BQQSPYJNBUJPO
     GPS
     NPEBM
     TPVOE 50( ڞ໐໰୊Λ࠷খݻ༗஋໰୊ͱͯ͠ఆࣜԽ͢Δ͜ͱʹΑΓ༗ݶཁૉ๏ʹ ج͍ͮͯڞৼप೾਺Λਪఆ͍ͯ͠Δ఺ Ի࣭఻ୡؔ਺Ͱ͸ͳ͘γες Ϝͷ܎਺ߦྻͷεϖΫτϧಛੑΛ࢖ͬͨ఺ 4*((3"1)
     "TJB
183. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ඞཁͳ৔ॴʹ஍৔Λ޲͚Δ͜ͱ͕ग़དྷΔઃܭՄೳͳి࣓ੴΛҹ࡮Մೳ ͳ%ϓϦϯλͷ։ൃ ి࣓ؾతཁૉΛ౓ͷҹ࡮Ͱ૊ΈࠐΊΔҹ࡮͕Ͱ͋Δ఺ ి࣓ੴͱͯ͠ͷੑೳͷଌఆͱͦΕΛ༻͍ͨΞϓϦέʔγϣϯͷ࡞੒ ߴిྲྀͰ͸ιϨϊΠυ͸௕ظؒͷىಈʹ͸ద͍ͯ͠ͳ͍఺ɺෳࡶͳߏ ଄Λ͢Δࡍ͸ΑΓࡉ͍మઢͱઈԑඃ෴Λআڈͷػߏ͕ඞཁͳ఺ 0OUIF'MZ
     1SJOU
     *ODSFNFOUBM
     1SJOUJOH
     8IJMF
     .PEFMJOH $)*
     1SJOUFE
     PQUJDT
     %
     QSJOUJOH
     PG
     FNCFEEFE
     PQUJDBM
     FMFNFOUT
     GPS
     JOUFSBDUJWF
     EFWJDFT 6*45 $)*ͰఏҊ͞Ε͍ͯΔ%0'%ϓϦϯλʹϫΠϠΧολʔͱϫΠ Ϡͷר͖ػߏΛඋ͑ͨϓϦϯλΛ࡞ͬͨ͜ͱɻ·ͨɺమઢΛమ৺ͱ͢ Δ͜ͱͰࣗ༝ͳߏ଄ʹରԠͰ͖ΔΑ͏ʹͨ͠఺ 6*45
184. ͲΜͳ΋ͷ Ͳ͏΍ͬͯ༗༻ੑΛݕূͨ͠ ઌߦݚڀͱൺ΂ͯͲ͕͍͜͢͝ ҧ͍ ʁ ٕज़΍ख๏ͷΩϞ͸Ͳ͜ʁ ٞ࿦͸͋Δ ࣍ʹಡΉ΂͖࿦จ 
     օ઒ୡ໵ َίʔε ฏ໘ͱۂ໘ͷ྆ํʹ൅ͷΑ͏ͳ࣍ݩܗঢ়ͷߏ଄Λҹ࡮͢Δख๏ ߴ͞ɺଠ͞ɺ֯౓ͳͲͷݸʑͷϔΞδΦϝτϦͱɺີ౓΍ҐஔͳͲͷ ϔΞΞϨΠͷϓϩύςΟΛݸผʹ੍ޚ͢Δ͜ͱ͕Ͱ͖Δ ࣮ࡍʹϞσϧΛ࡞੒ͯ͠Έͨɻ·ͨɺ࣮ࡍͷΞϓϦέʔγϣϯ ύο γϒΞΫνϡΤʔλɺٵ൫ Λҹ࡮ͨ͠ɻ ͜ͷߏ଄Ͱ෴ΘΕͨϞσϧΛ࡞Δʹ͸෼ׂ͢Δඞཁ͕͋Δɻۂ໘্ʹ ͜ͷߏ଄Λ഑ஔ͢Δਫ਼౓͸ۂ໘ͷϝογϡղ૾౓ʹґଘ͢Δɻ 4FOTJOH
     5ISPVHI
     4USVDUVSF
     %FTJHOJOH
     4PGU
     4JMJDPOF
     4FOTPST 5&* ൅ͷໟͷԁਲ਼ϞσϧΛ%-1ͷϓϦδΣΫλͷυοτʹۙࣅ͍ͯ͠Δ $)*
187. DEEPMIMIC: EXAMPLE-GUIDED DEEP REINFORCEMENT LEARNING OF PHYSICS-BASED CHARACTER SKILLS XUE BIN PENG PIETER ABBEEL SERGEY LEVINE MICHIEL VAN DE PANNE न॒ऩुभء ੔ষଢ଼஢धૻसथनऒऋघओःء ૼ୒ृু১भय़ঔ नअृढथથ஍टधਫ਼઒खञء ৮૛मँॊء ઃपഭिसऌ૛ધमء ঔش३ঙথय़কউॳকॡজॵউृय़شইঞش঒॔ॽওش३ঙথ भ஄द઀୹औोॊؚॹشॱप৯৓ಂ਱भਘ৲৾ಆ॑ੌा়ॎच ञ੟৶ঋش५भय़কছॡॱ॔ॽওش३ঙথभইঞش঒ডشॡ ౐঳भঔॹঝইজشभইঞش঒ডشॡऋेॉઁः෇ ೧भঔش३ঙথ५य़ঝऋँॉؚऽञजोै॑ದ๰હ ऐॊચৡ॑੅ढथःॊ deep reinforcement learningभ॔ঝ०জ६঒ [Duan et al. 2016]ऩन॑ઞ৷ ५य़ঝ॑஘رऩय़কছॡॱشؚ୭୆ؚउेलॱ५ॡपজ ॱش।ॸॕথॢखؚളਯभृॉ্॑ੌा়ॎचथളਯभ ५य़ঝ॑ৰষदऌॊेअपखञ నষؚ࿼ฺऩඝ੿ؚजभ౎भ੿঵पి৷औोॊेअपؚ ট঎ॵॺ३५ॸ঒पএজ३ش॑नभेअपଦ૟घॊऊ [Duan et al. 2016] DeepLoco 201511520 ൝਽࿜མ(য৑॥ش५) SIGGRAPH2018
188. DEEPLOCO: DYNAMIC LOCOMOTION SKILLS USING HIERARCHICAL DEEP REINFORCEMENT LEARNING XUE BIN PENG GLEN BERSETH KANGKANG YIN MICHIEL VAN DE PANNE न॒ऩुभء ੔ষଢ଼஢धૻसथनऒऋघओःء ૼ୒ृু১भय़ঔ नअृढथથ஍टधਫ਼઒खञء ৮૛मँॊء ઃपഭिसऌ૛ધमء ଒ैोञ୤भহ৐ੴ௙॑੅ण୭୆पଦൟखञ ஘رऩઈ৿५य़ঝ॑৾ಆघॊ ୆্ଉभ3D੸ଌనষ५य़ঝऋؚ଒ैोञ୤भ੔भଡ ୗऋ਑౪এজ३شपୖचैोथ৾ಆऔोॊऒध॑ৰ ઒घॊ ਑౪এজ३ش॑৾ಆघॊञीभdeep Reinforcement LearningقRLكभਃચ॑ણ৷ నষ५य़ঝभdeep Reinforcement Learningभञीभ2 ঞঋঝమಽभઞ৷ ્৒भHLCॱ५ॡ॑ઉம१এشॺघॊ୯ਸभLLC५य़ঝ ॑ಆ੭घॊ্১॑ৄणऐॊऒधऋ০৏੎ਏ [Liu et al. 2012] 201511520 ൝਽࿜མ(য৑॥ش५) SIGGRAPH2017
189. SIMULATION OF HUMAN MOTION DATA USING SHORT-HORIZON MODEL-PREDICTIVE CONTROL M. da Silva, Y. Abe, and J. Popovic न॒ऩुभء ੔ষଢ଼஢धૻसथनऒऋघओःء ૼ୒ृু১भय़ঔ नअृढथથ஍टधਫ਼઒खञء ৮૛मँॊء ઃपഭिसऌ૛ધमء ३঑গঞشॺऔोञয৑॑ৰ੠भ৿ऌॹشॱप଑ऎऒध पेढथؚঽேऩ৿ऌ॑েਛघॊঔॹঝ੒೾॥থॺটش ছMcSim॑઀ં হ৐ੑ઴धু৿भॳগشॽথॢमऺध॒नऩऎؚ੦૆৿੿ ध৊஘पనষؚ௒ষؚ४কথউ৿੿॑েਛघॊऒधऋदऌ ऽघऋؚৌਵச২दৗखः੟৶୭୆قl7كपुిૢघॊ ੒೾॥থএشॿথॺध଩।ॖথभૻ୻๶ী قPDك॥থএشॿথॺقl5ك॑ੌा়ॎचॊ জথॡऔोञྌ৬धமඡৡ৾भ଍஄৲ঔॹঝप੦तऎ੒೾॥থ এشॿথॺقl4ك॑ઞ৷ؚ଍஄৿ৡ৾ঔॹঝमোৡઈ৿॑୯඄ घॊঢ়තৡउेलਗৡ॑ੰৠघॊ੸ઃউটॢছ঒قQPكभ਑৺ धखथઞ৷ ঳৖भ3Dঔش३ঙথभਈిऩঃছওشॱ॑ৄणऐॊऒध ऋ൑୔दँढञ؛৚ૡघॊ৿ऌऩनअऽऎ୯జदऌऩः ৿੿ुँढञ؛ >$)2@$5,.$12)256<7+'$2·%5,(1-) Motion synthesis from annotations. ACM Transactions on Graphics 22, 3 (July 2003), 402²408. 201511520 ൝਽࿜མ(য৑॥ش५) EUROGRAPHICS 2008
190. A DEEP LEARNING FRAMEWORK FOR CHARACTER MOTION SYNTHESIS AND EDITING Daniel Holden, Jun Saito, Taku Komura न॒ऩुभء ੔ষଢ଼஢धૻसथनऒऋघओःء ૼ୒ृু১भय़ঔ नअृढथથ஍टधਫ਼઒खञء ৮૛मँॊء ઃपഭिसऌ૛ધमء ু੿঵पेॊ৐૪৶ृয৑भু॑ઞॎङपؚপ୤भয৑भ৿੿ॹشॱ॑ ৷ःथؚశ଍஄੗஘৬षभ৿੿ॹشॱभඇी੢ा॑ঽ৿৓प৾ಆदऌॊ ஥ः৾ಆইঞش঒ডشॡप੦तऎ॔ॽওش३ঙথभ়ਛधౣૐभঔॹঝ ॑઀੧ উট७५॑෩खऎઍಔपघॊ৿ऌ७ॢওথॺ৲ऽञ मਜ਼઼়ॎच॑૑ਏधखऩः ৈঞঋঝभঃছওشॱش॑଩ঞঋঝभয৑भ৿ ऌपঐॵউघॊશभইॕشॻইज़ডشॻॽগش ছঝॿॵॺডشॡ॑஋ा੎बॊ পऌऩঔش३ঙথॹشॱঋش५॑ઞ৷खथঔش३ঙথঐ ॽ঍ঝॻ॑ਈੂप৾ಆखؚઃःदؚঘش२োৡधলৡ ঔش३ঙথधभ৑भঐॵআথॢ॑েਛघॊ ५ॱॵॡऔोञॹॕشউड़شॺग़থ॥شॲ॑ঔش३ঙথঐॽ ঍شঝॻभ৾ಆपઞ৷घॊधؚॺঞشॽথॢऋઍಔऩेॉ౐ෞ ऩইॕشॻইज़ডشॻॿॵॺডشॡ॑ઞ৷दऌॊ [Hariharan et al. 2014] 201511520 ൝਽࿜མ(য৑॥ش५) SIGGRAPH2016
191. DIVERSE MOTION VARIATIONS FOR PHYSICS-BASED CHARACTER ANIMATION Shailen Agrawal, Shuo Shen, Michiel van de Panne न॒ऩुभء ੔ষଢ଼஢धૻसथनऒऋघओःء ૼ୒ृু১भय़ঔ नअृढथથ஍टधਫ਼઒खञء ৮૛मँॊء ઃपഭिसऌ૛ધमء ੐৒औोञல஘॑௥ञघ੗஘ऩঔش३ঙথংজग़ش३ঙ থ॑ঽ৿৓पেਛघॊऒध॑઀੧ ੗஘ਙभਈి৲॑ઞ৷खथؚਂેীऩ৿ऌऋ୸ਛऔ ो੭ॊ੗ऎभ૭ચऩ্১पऽञऋॊ३঑গঞشॺऔ ोञ৿ऌभૐ়॑૭ચपघॊ ੟৶৓ऩय़কছॡॱ॔ॽওش३ঙথभ੗஘ਙਈ ి৲प્৲खञ৯৓ঢ়ਯؚছक़থॻটঅথુী ങষഔిૢقCMAك॑ઞ৷ ৿ऌभ੗஘ਙ॑೾৒घॊञीभ෱௞ওॺজॵॡधखथಉ खऎిखथःॊधੴैोथःॊএش६థ๚ਙওॺজॵॡ भઞ৷ ્৒भৎਡद્৒भय़شইঞش঒पਊञॊऩनؚడഡप਑৺औ ोञঔش३ঙথमؚ੗஘ਙ॑ਈి৲घॊञीभ৖ী૬৑ऋ଒ै ोथउॉ્৒भ਑৺॑௥ञघऒधऋ൑୔पऩॊ BORNO, M. A., DE LASA, M., AND HERTZMANN, A. 2013. Trajectory optimization for full-body movements with complex contacts. IEEE Trans. Visualization and Computer Graphics. in press. 201511520 ൝਽࿜མ(য৑॥ش५) 6&$·
192. PHASE-FUNCTIONED NEURAL NETWORKS FOR CHARACTER CONTROL DANIEL HOLDEN, TAKU KOMURA, JUN SAITO न॒ऩुभء ੔ষଢ଼஢धૻसथनऒऋघओःء ૼ୒ृু১भय़ঔ नअृढथથ஍टधਫ਼઒खञء ৮૛मँॊء ઃपഭिसऌ૛ધमء ਜ਼ৼਃચॽগشছঝॿॵॺডشॡधళयोॊৗखः ॽগشছঝॿॵॺডشॡ॔شय़ॸॡॳক॑৷ःथؚজ॔ ঝॱॖ঒य़কছॡॱ਑౪ਃଡ॑઀ં औऽकऽऩ࿥୦৾৓୭୆पిૢघॊঔش३ঙথ॑ঽ ৿৓पেਛघॊऒधऋदऌॊ ਜ਼ৼॽগشছঝॿॵॺডشॡقPhaseNunction Network؟PFNNكधళयोॊॽগشছঝॿॵॺডشॡ ଡୗ ൽः৉஄ृລඩ॑৚ೂखؚ௽૩॑ೂऐॊय़কছॡॱभञ ीभిજद਀ਠৡभँॊ୎৿॑ঽ৿৓पেਛ ॥থঃॡॺऩଡୗदुؚৎ৑भ৽ૌधधुप৞ෟप૗৲घॊਜ਼ ৼঢ়ਯपेॉؚॿॵॺডشॡଡਛभপऌऩ૗৲॑েाলघऒध पेढथপऌऩৈઃ੪भॹشॱ७ॵॺऊै৾ಆदऌॊ [Holden et al. 2016, 2015] 201511520 ൝਽࿜མ(য৑॥ش५) SIGGRAPH2017