{"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\/hr\/blog\/news\/otkryt-kod-sistemy-mashinnogo-obucheniya-dlya-generaczii-realistichnyh-dvizhenij-cheloveka","title":{"rendered":"Otvoreni su kodovi sustava strojnog u\u010denja za generiranje realisti\u010dnih pokreta \u010dovjeka.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Grupa istra\u017eiva\u010da sa Sveu\u010dili\u0161ta u Tel Avivu objavila je izvorne tekstove povezane sa sustavom strojnog u\u010denja MDM (Motion Diffusion Model), koji omogu\u0107uje generiranje realisti\u010dnih pokreta \u010dovjeka. Kod je napisan na jeziku Python koriste\u0107i framework PyTorch i distribuira se pod MIT licencom. Za provo\u0111enje eksperimenata mogu se koristiti ve\u0107 gotovi modeli, ili se mo\u017ee provesti trening modela samostalno pomo\u0107u ponu\u0111enih skripti, na primjer, koriste\u0107i kolekciju trodimenzionalnih slika \u010dovjeka HumanML3D. Za obuku sustava potrebna je GPU koja podr\u017eava CUDA. <\/p>\n

Primjena tradicionalnih mogu\u0107nosti za animiranje pokreta \u010dovjeka ote\u017eana je zbog komplikacija povezanih s velikim raznolikostima mogu\u0107ih pokreta i pote\u0161ko\u0107e u njihovom formalnom opisu, kao i zbog velike osjetljivosti ljudske percepcije na neprirodne pokrete. Ranije poku\u0161aji kori\u0161tenja generativnih modela strojnog u\u010denja imali su problema s kvalitetom i ograni\u010denom izra\u017eajno\u0161\u0107u. <\/p>\n

U predlo\u017eenom sustavu poduzete su mjere za kori\u0161tenje difuzijskih modela za generiranje kretanja, koji su po svojoj su\u0161tini bolje prilago\u0111eni za simulaciju ljudskih pokreta, no nisu li\u0161eni nedostataka, poput visokih zahtjeva za ra\u010dunalnim resursima i slo\u017eenosti upravljanja. Kako bi se minimizirali nedostaci difuzijskih modela, u MDM-u je anga\u017eirana neuronska mre\u017ea s arhitekturom \u201etransformer\u201d i predvi\u0111anjem uzorka umjesto predvi\u0111anja \u0161uma u svakoj fazi, \u0161to olak\u0161ava sprje\u010davanje anomalija, poput gubitka kontakta povr\u0161ine s nogom. <\/p>\n

Za upravljanje generiranjem predvi\u0111ena je mogu\u0107nost kori\u0161tenja tekstualnog opisa akcije na prirodnom jeziku (na primjer, \u201e\u010dovjek ide naprijed i sagne se kako bi podigao ne\u0161to s tla\u201d) ili kori\u0161tenje standardnih akcija, poput \u201etr\u010danja\u201d i \u201eska\u010du\u0107i\u201d. Sustav se tako\u0111er mo\u017ee koristiti za ure\u0111ivanje pokreta i popunjavanje izgubljenih detalja. Istra\u017eiva\u010di su proveli testiranje u kojem su sudionici trebali odabrati kvalitetniji rezultat me\u0111u nekoliko opcija \u2014 u 42% slu\u010dajeva ljudi su dali prednost sintetiziranim pokretima umjesto stvarnim.

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

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