{"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\/pl\/blog\/news\/otkryt-kod-sistemy-mashinnogo-obucheniya-dlya-generaczii-realistichnyh-dvizhenij-cheloveka","title":{"rendered":"Otwarty kod systemu uczenia maszynowego do generowania realistycznych ruch\u00f3w cz\u0142owieka","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Zesp\u00f3\u0142 badaczy z Uniwersytetu w Tel Awiwie opublikowa\u0142 kody \u017ar\u00f3d\u0142owe zwi\u0105zane z systemem uczenia maszynowego MDM (Motion Diffusion Model), kt\u00f3ry umo\u017cliwia generowanie realistycznych ruch\u00f3w ludzkich. Kod napisano w j\u0119zyku Python z wykorzystaniem frameworka PyTorch i jest dystrybuowany na licencji MIT. Do przeprowadzania eksperyment\u00f3w mo\u017cna wykorzysta\u0107 zar\u00f3wno gotowe modele, jak i przeprowadzi\u0107 trening modeli samodzielnie za pomoc\u0105 dostarczanych skrypt\u00f3w, na przyk\u0142ad korzystaj\u0105c z kolekcji tr\u00f3jwymiarowych obraz\u00f3w ludzi HumanML3D. Do uczenia systemu wymagany jest GPU z obs\u0142ug\u0105 CUDA. <\/p>\n

Zastosowanie tradycyjnych mo\u017cliwo\u015bci animowania ruch\u00f3w ludzkich jest utrudnione z powodu komplikacji zwi\u0105zanych z du\u017c\u0105 r\u00f3\u017cnorodno\u015bci\u0105 mo\u017cliwych ruch\u00f3w i trudno\u015bci ich formalnego opisu oraz z powodu du\u017cej wra\u017cliwo\u015bci ludzkiego postrzegania na nienaturalne ruchy. Wcze\u015bniejsze pr\u00f3bki wykorzystania generatywnych modeli uczenia maszynowego mia\u0142y problemy z jako\u015bci\u0105 i ograniczon\u0105 ekspresyjno\u015bci\u0105. <\/p>\n

W proponowanym systemie podj\u0119to pr\u00f3b\u0119 wykorzystania modeli dyfuzyjnych do generowania ruch\u00f3w, kt\u00f3re w swoim istocie lepiej nadaj\u0105 si\u0119 do symulacji ludzkich ruch\u00f3w, ale nie s\u0105 pozbawione wad, takich jak wysokie wymagania dotycz\u0105ce zasob\u00f3w obliczeniowych i z\u0142o\u017cono\u015b\u0107 zarz\u0105dzania. Aby zminimalizowa\u0107 wady modeli dyfuzyjnych, w MDM zosta\u0142a zaanga\u017cowana sie\u0107 neuronowa o architekturze \u201etransformer\u201d oraz prognozowanie pr\u00f3bki (sample) zamiast prognozowania szumu na ka\u017cdym etapie, co upraszcza zapobieganie anomaliom, takim jak utrata kontaktu powierzchni z nog\u0105. <\/p>\n

Do zarz\u0105dzania generacj\u0105 przewidziano mo\u017cliwo\u015b\u0107 zastosowania opisu dzia\u0142ania w naturalnym j\u0119zyku (na przyk\u0142ad \u201ecz\u0142owiek idzie naprz\u00f3d i pochyla si\u0119, aby podnie\u015b\u0107 co\u015b z ziemi\u201d) lub wykorzystania typowych akcji, takich jak \u201ebieg\u201d i \u201eskoki\u201d. System mo\u017cna r\u00f3wnie\u017c stosowa\u0107 do edytowania ruch\u00f3w oraz uzupe\u0142niania utraconych szczeg\u00f3\u0142\u00f3w. Badacze przeprowadzili testowanie, w ramach kt\u00f3rego uczestnicy mieli za zadanie wybra\u0107 z kilku opcji bardziej jako\u015bciowy rezultat \u2014 w 42% przypadk\u00f3w ludzie preferowali syntetyzowane ruchy zamiast rzeczywistych.

\r\n \"Odtwarzaj\r\n
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\u0179r\u00f3d\u0142o: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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