{"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":"Ava on avatud masin\u00f5ppe s\u00fcsteemi kood, mis genereerib realistlikke inimliikumisi.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Tel-Avivi \u00fclikooli teadlaste r\u00fchm avaldas MDM (Motion Diffusion Model) masin\u00f5ppes\u00fcsteemiga seotud l\u00e4htekoodid, mis v\u00f5imaldab genereerida realistlikke inimliikumisi. Kood on kirjutatud Python'i keeles ja kasutab PyTorch raamistikku, jagatakse MIT litsentsi alusel. Eksperimentide jaoks saab kasutada nii valmis mudeleid kui ka koolitada mudeleid ise, kasutades pakutud skripte, n\u00e4iteks inimese kolme m\u00f5\u00f5tme piltide kogumit HumanML3D. S\u00fcsteemi koolitamiseks on vajalik CUDA toetav GPU. <\/p>\n

Inimeste liikumise animeerimise traditsiooniliste v\u00f5imaluste rakendamine on keeruline, kuna on palju erinevaid v\u00f5imalikke liikumisi ja nende formaalne kirjeldamine on keeruline. Inimese tajumise tundlikkus ebaloomulike liikumiste suhtes toob endaga kaasa lisaraskusi. Varasemad katsed generatiivsete masin\u00f5ppemudelite kasutamisega olid seotud kvaliteedi ja piiratud v\u00e4ljendusrikkuse probleemidega. <\/p>\n

Pakutud s\u00fcsteemis on p\u00fc\u00fctud kasutada difusioonimudeleid liigutuste genereerimiseks, mis on oma olemuselt paremad inimeste liikumiste simuleerimiseks, kuid ei ole vabad puudustest, nagu suured arvutusressursside n\u00f5udmised ja keeruline haldamine. Difusioonimudelite puuduste minimeerimiseks on MDM-is kaasatud n\u00e4rviv\u00f5rk arhitektuuriga 'transformer' ja n\u00e4idise prognoosimine (sample) iga etapi m\u00fcra ennustamise asemel, mis lihtsustab anomaaliate, nagu jala ja pinna kontaktikadu, ennetamist. <\/p>\n

Geneerimise juhtimiseks on v\u00f5imalik kasutada looduskeeles toimuva tegevuse tekstilist kirjeldust (n\u00e4iteks 'inimene k\u00f5nnib edasi ja kummardab, et midagi maast \u00fcles korjata') v\u00f5i kasutada t\u00fc\u00fcpilisi tegevusi, nagu 'jooks' ja 'h\u00fcpped'. S\u00fcsteemi saab kasutada ka liikumiste redigeerimiseks ja kaotatud detailide t\u00e4iendamiseks. Uurijad viisid l\u00e4bi katse, kus osalistele pakuti valida mitme variandi hulgast kvaliteetsem tulemus \u2014 42% juhtudest andsid inimesed eelise s\u00fcnteesitud liikumistele, mitte reaalsetele.

\r\n \"Vaata\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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