{"id":38674,"date":"2019-10-31T22:25:15","date_gmt":"2019-10-31T19:25:15","guid":{"rendered":"https:\/\/prohoster.info\/blog\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej\/"},"modified":"2019-10-31T22:25:15","modified_gmt":"2019-10-31T19:25:15","slug":"otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sv\/blog\/news\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"Koden f\u00f6r att syntetisera animation med hj\u00e4lp av neurala n\u00e4tverk har sl\u00e4ppts","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

En grupp forskare fr\u00e5n det tekniska universitetet i Shanghai publicerade<\/a><\/noindex> verktyg Impersonator<\/a><\/noindex>, som anv\u00e4nder maskininl\u00e4rningsmetoder f\u00f6r att simulera m\u00e4nniskors r\u00f6relser p\u00e5 stillbilder samt byta kl\u00e4der, flytta till en annan milj\u00f6 och \u00e4ndra vinkel f\u00f6r hur objektet ses. Koden \u00e4r skriven i Python
\nmed hj\u00e4lp av ramverket PyTorch<\/a><\/noindex>. F\u00f6r byggande kr\u00e4vs \u00e4ven torchvision<\/a><\/noindex> och CUDA Toolkit. <\/p>\n

\"Koden<\/center><\/p>\n

Verktyget tar emot en tv\u00e5dimensionell bild och syntetiserar ett modifierat resultat baserat p\u00e5 den valda modellen. Tre typer av transformationer st\u00f6ds:
\nSkapande av ett r\u00f6rligt objekt som \u00e5terspeglar de r\u00f6relser som modellen har tr\u00e4nats p\u00e5. \u00d6verf\u00f6ring av utseendelement fr\u00e5n modellen till objektet (till exempel byte av kl\u00e4der). Generering av en ny vinkel (till exempel syntetisering av en profilbild baserat p\u00e5 en framifr\u00e5n-fotografi). Alla tre metoder kan kombineras; till exempel kan man generera en video fr\u00e5n ett foto som imiterar en komplex akrobatisk trick i andra kl\u00e4der. <\/p>\n

\r\n \"Spela\r\n
<\/div>\r\n <\/div><\/center><\/p>\n

Under syntesprocessen utf\u00f6rs \u00e4ven operationer f\u00f6r att isolera objektet p\u00e5 fotot och skapa saknade bakgrundselement vid f\u00f6rflyttning. Modellen f\u00f6r det neurala n\u00e4tverket kan tr\u00e4nas en g\u00e5ng och anv\u00e4ndas f\u00f6r olika transformationer. F\u00f6r att ladda ner \u00e4r tillg\u00e4ngliga<\/a><\/noindex> f\u00e4rdiga modeller som till\u00e5ter att verktyget anv\u00e4nds direkt utan f\u00f6rhandsutbildning. F\u00f6r arbete kr\u00e4vs en GPU med en minnesstorlek p\u00e5 minst 8GB.<\/p>\n

Till skillnad fr\u00e5n transformationsmetoder baserade p\u00e5 nyckelpunkter som beskriver kroppens placering i tv\u00e5dimensionellt utrymme, g\u00f6r Impersonator ett f\u00f6rs\u00f6k att syntetisera ett tredimensionellt n\u00e4t (mesh) med en beskrivning av kroppen, med hj\u00e4lp av maskininl\u00e4rningsmetoder.
\nDen f\u00f6reslagna metoden m\u00f6jligg\u00f6r manipulationer med h\u00e4nsyn till den personliga kroppstypen och den aktuella posen, vilket modellerar naturliga r\u00f6relser av lemmarna. <\/p>\n

\"Koden<\/a><\/noindex><\/center><\/p>\n

F\u00f6r att bevara ursprungsinformation, s\u00e5som texturer, stil, f\u00e4rger och igenk\u00e4nning av ansiktet, till\u00e4mpas en generativ konkurrensutsatt neuralt n\u00e4tverk<\/a><\/noindex> (Liquid Warping GAN<\/a><\/noindex>). Information om det ursprungliga objektet och parametrar f\u00f6r dess noggranna identifiering extraheras genom att till\u00e4mpa ett konvolutionellt neuralt n\u00e4tverk<\/a><\/noindex>.<\/p>\n

\r\n \"Spela\r\n
<\/div>\r\n <\/div>
\n<\/center><\/p>\n

<\/a><\/noindex><\/p>\n

K\u00e4lla: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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