{"id":105302,"date":"2022-10-13T15:36:48","date_gmt":"2022-10-13T13:36:48","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/otkryt-kod-sistemy-mashinnogo-obucheniya-dlya-generaczii-realistichnyh-dvizhenij-cheloveka"},"modified":"2022-10-13T15:36:48","modified_gmt":"2022-10-13T13:36:48","slug":"otkryt-kod-sistemy-mashinnogo-obucheniya-dlya-generaczii-realistichnyh-dvizhenij-cheloveka","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/news\/otkryt-kod-sistemy-mashinnogo-obucheniya-dlya-generaczii-realistichnyh-dvizhenij-cheloveka","title":{"rendered":"Avatud on masin\u00f5ppe s\u00fcsteemi kood realistlike inimeste liikumiste genereerimiseks","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Tel Avivi \u00fclikooli teadlaste grupp avas l\u00e4htekoodid, mis on seotud masin\u00f5ppe s\u00fcsteemiga MDM (Motion Diffusion Model), mis v\u00f5imaldab genereerida realistlikke inimeste liikumisi. Kood on kirjutatud Pythonis, kasutades PyTorchi raamistikku, ja seda levitatakse MIT-litsentsi alusel. Eksperimentide l\u00e4biviimiseks saab kasutada nii ettevalmistatud mudeleid kui ka treenida mudeleid iseseisvalt pakutavate skriptide abil, n\u00e4iteks kasutades kolme m\u00f5\u00f5tmeliste inimeste piltide kogu HumanML3D. S\u00fcsteemi \u00f5petamiseks on vajalik CUDA toega GPU. <\/p>\n

Tavap\u00e4raste v\u00f5imaluste rakendamine inimese liikumiste animeerimiseks on keeruline, kuna suur liikuvate v\u00f5imaluste mitmekesisus ja nende formaalse kirjeldamise raskused, samuti inimese tajumise suur tundlikkus ebatavaliste liikumiste suhtes. Varasemad katsed kasutada genereerivaid masin\u00f5ppe mudeleid seisid silmitsi kvaliteedi ja piiratud v\u00e4ljendusv\u00f5ime probleemidega. <\/p>\n

Pakutud s\u00fcsteemis on proovitud kasutada difusioonimudeleid liikumiste genereerimiseks, mis oma olemuselt sobivad paremini inimeste liikumiste simuleerimiseks, kuid ei ole vabad puudustest, nagu k\u00f5rged arvutusressursside n\u00f5udmised ja haldamise keerukus. Difusioonimudelite puuduste minimeerimiseks on MDM-is kasutatud \u201etransformeri\u201c arhitektuuriga n\u00e4rviv\u00f5rku ja n\u00e4idise prognoosimist (sample) igas etapis m\u00fcra prognoosimise asemel, mis lihtsustab anomaaliate v\u00e4ltimist, nagu n\u00e4iteks pealse ja jala kaotamine. <\/p>\n

Generatsiooni juhtimiseks on ette n\u00e4htud v\u00f5imalus kasutada loomulikus keeles tegevuse kirjeldust (n\u00e4iteks \u201einimene astub edasi ja kummardub, et maapinnalt midagi \u00fcles korjata\u201c) v\u00f5i kasutada t\u00fc\u00fcpilisi tegevusi, nagu \u201ejooks\u201c ja \u201eh\u00fcpped\u201c. S\u00fcsteemi saab rakendada ka liikumiste redigeerimiseks ja kaotatud detailide t\u00e4iustamiseks. Uurijad viisid l\u00e4bi testimise, mille k\u00e4igus osalejatel paluti valida mitme variandi seast kvaliteetseim tulemus - 42% juhtudest eelistasid inimesed s\u00fcnteesitud liikumisi, mitte reaalseid.

\r\n \"M\u00e4ngi\r\n
<\/div>\r\n <\/div><\/center>
\n
Allikas: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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